nonparametric statistics for non-statisticians: a step-by-step approach by gregory w. corder, dale...

International Statistical Review (2010), 78, 3, 445–482 doi:10.1111/j.1751-5823.2010.00122.x

Short Book ReviewsEconophysics and Companies: Statistical Life and Death in Complex Business NetworksHideaki Aoyama, Yoshi Fujiwara, Yuichi Ikeda, Hiroshi Iyetomi, Wataru SoumaCambridge University Press, 2010, xxvii + 234 pages, £ 60.00 / US$ 99.00, hardcoverISBN: 978-0-521-19149-4

Table of contents

1. New insights 4. Complex business networks2. Size distribution 5. An agent-based model for companies3. Company growth as fluctuations 6. Perspectives for practical applications

Readership: Economists, statisticians, and researchers in these disciplines.

Econophysics is a relatively new “discipline” that seeks to model economic behaviours as massphenomena analogous to the way statistical physics describes macroscopic phenomena in termsof the interactions between huge numbers of microscopic elements. Readers familiar with theworks of Isaac Asimov will recognize the notion of “psychohistory” from his Foundation trilogy.Readers familiar with the history of statistics will note the full circle: statistical physics wasoriginally developed by analogy with government statistical summaries of populations of people.

Econophysics has always seemed to me to be a nice idea in principle, but in practice Ihave always expected non-linearities and interactions to mean that only a limited numberof elementary reproducible phenomena would be observed. This book has not convincedme otherwise. Moreover, the phenomena it describes are really basic statistical or stochasticphenomena, and to relate them to physics seems far-fetched. It discusses such things as powerlaw distributions, copulas, and the statistics of networks, all applied in an interesting way todata from companies, but none really having any special relationship to physics, and I think alldiscussed elsewhere in fairly standard statistical texts. This is perhaps curious, since the authorsare five physicists and one economist, and the last of these wrote the foreword.

Nevertheless, despite the above, the book certainly gives examples of interesting regularitiesin statistical distributions in data relating to Japanese companies. From that perspective, itis a thought-provoking book. It is also attractively prepared, and generally elegantly written.However, be warned that it departs surprisingly from standard usage in places. Statistical readers,for example, will be alarmed to discover, on page 19, that the cumulative distribution functionat x is the proportion of values greater than x. (This is not a simple typo: page 20 defines “avariation of the CDF” as the proportion of data with values smaller than x, and page 35 describesa CDF as a decreasing function of x.)

Overall, an interesting and thought-provoking book, but the description “econophysics” seemsa step too far; “econostatistics” seems nearer the mark.

David J. Hand: [email protected] Department, Imperial College

London SW7 2AZ, UK

C© 2010 The Authors. International Statistical Review C© 2010 International Statistical Institute. Published by Blackwell Publishing Ltd, 9600 GarsingtonRoad, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

446 SHORT BOOK REVIEWS

Permutation Tests for Stochastic Ordering and ANOVA: Theory and Applications with RDario Basso, Fortunato Pesarin, Luigi Salmaso, Aldo SolariSpringer, 2009, xiv + 218 pages, £ 49.99 / € 54.95 / US$ 69.95, softcoverISBN: 978-0-387–85955-2

Table of contents

1. Permutation tests Part II: Nonparametric ANOVAPart I: Stochastic Ordering 5. Nonparametric one-way ANOVA2. Ordinal data 6. Synchronized permutation tests in two-way ANOVA3. Multivariate ordinal data 7. Permutation tests for unreplicated factorial designs4. Multivariate continuous data

Readership: Students in courses on permutation methods and theoretical or applied statisticiansinterested in permutation methods.

As the book’s title implies, the basis of this book is the permutation approach to statisticalinference. Specifically, the permutation methods are applied to univariate and multivariatehypothesis testing problems for ordered data and data from experimental designs. The authorsemphasize the distribution-free and non-parametric nature of permutations test, in contrast tostandard parametric methods that have assumptions which are not always adequate. Thus, alsocompeting parametric methods are discussed to some extent. The text contains the necessaryformulas and algorithms for those who want to study the theoretical foundation of the methodsthat are presented, but also examples with R code and output from applications of the methodsthat are useful for the more applied statistician. All R code and functions used in the book arealso available online from an accompanying web site. The book could be used as a textbook foran advanced course in statistics covering these subjects or as a reference book for theoretical orapplied statisticians interested in permutation tests.

Andreas Rosenblad: [email protected] for Clinical Research Vasteras, Uppsala University

Central Hospital, S-721 89 Vasteras, Sweden

International Statistical Review (2010), 78, 3, 445–482C© 2010 The Authors. International Statistical Review C© 2010 International Statistical Institute

SHORT BOOK REVIEWS 447

Measurement Error: Models, Methods, and ApplicationsJohn P. BuonaccorsiChapman & Hall/CRC, 2010, xxvi + 437 pages, £ 57.99 / US$ 89.95, hardcoverISBN: 978-1-4200-6656-2

Table of contents

1. Introduction 8. Linear models with nonadditive error2. Misclassification in estimating a proportion 9. Nonlinear regression3. Misclassification in two-way tables 10. Error in the response4. Simple linear regression 11. Mixed/longitudinal models5. Multiple linear regression 12. Time series6. Measurement error in regression: a general overview 13. Background material7. Binary regression

Readership: Applied statisticians, users of statistics.

Measurement error occurs when a true value x is recorded as a different value w. The variablesmay be quantitative, when w is often a numerical approximation to x, or categorical, when w andx represent class membership. Also, they may be response variables or explanatory/predictorvariables. All these cases are covered in this book, which considers what consequences followfrom ignoring the problem (by conducting a “naıve” analysis) and how to correct the results oncethe problem is recognized. The author says that the work is “all non-Bayesian” and that the levelis “more applied” than previous books on the subject. The target audience is broad, comprisingapplied statisticians and others involved in practical statistics: the computing demonstrations aregiven in STATA and SAS, which are commonly used in the medical and social sciences, amongothers.

Chapter 1 sets out the basic ideas. The true value x is observed as w due to recording error,or having to estimate x from other measures, or sampling error, etc. The main ingredients are(i) a model for the true values, (ii) a model for measurement error, and (iii) additional data,information or assumptions to enable correction. The classical measurement error model is basedon an assumption about p(w | x), whereas the Berkson error model is based on p(x | w): which isappropriate depends on the setting. Chapters 2 and 3 deal with estimating a proportion (binomialand multinomial), and two-way tables. Chapters 4–6 cover simple linear regression (includingfunctional and structural relations), multiple linear regression, and a general regression overview(a long chapter including moment-based corrections and estimating equations). Chapters 7–12cover some particular classes of models, extending the previous applications to more specializedsituations.

Some chapters include a section at the end, “Mathematical Developments,” detailing thealgebraic expressions underlying the methods covered; this is also done in Chapter 13, effectivelyan appendix. There are plenty of illustrations and worked examples throughout but no exercisesfor the reader. The bibliography is pretty comprehensive (15 pages), including 16 of the author’sown publications as sole or co-author.

The book is very readable and clearly demonstrates the importance of recognizing measure-ment error, which is often ignored as a bit of a nuisance to be swept under the carpet. Togetherwith easily-accessible software in the future the problem is likely to be more commonly addressedand dealt with properly.

Martin Crowder: [email protected] Department, Imperial College

London SW7 2AZ, UK



Biological Data MiningJake Y. Chen, Stefano Lonardi (Editors)Chapman & Hall/CRC, 2010, xx + 713 pages, £ 63.99 / US$ 99.95, hardcoverISBN: 978–1-4200–8684-3

Table of contents

Part I. Sequence, Structure, and Function Part III. Functional and Molecular InteractionNetworks1. Consensus structure prediction for RNA

alignments (Junilda Spirollari,Jason T. L. Wang) 15. Inferring protein functional linkage based on

sequence information and beyond (Li Liao)2. Invariant geometric properties of secondarystructure elements in proteins (Matteo Comin,Concettina Guerra, Giuseppe Zanotti)

16. Computational methods for unravellingtranscriptional regulatory networks inprokaryotes (Dongsheng Che, Guojun Li)3. Discovering 3D motifs in RNA (Alberto

Apostolico, Giovanni Ciriello, ConcettinaGuerra, Christine E. Heitsch)

17. Computational methods for analysing andmodelling biological networks (Natasa Przulj,Tijana Milenkovic)4. Protein structure classification using machine

learning methods (Yazhene Krishnaraj,Chandan Reddy)

18. Statistical analysis of biomolecular networks(Jing-Dong J. Han, Chris J. Needham)

5. Protein surface representation and comparison:new approaches in structural proteomics (LeeSael, Daisuke Kihara)

Part IV. Literature, Ontology, and KnowledgeIntegration

19. Beyond information retrieval: literature miningfor biomedical knowledge discovery (JavedMostafa, Kazuhiro Seki, Weimao Ke)

6. Advanced graph mining methods for proteinanalysis (Yi-Ping Phoebe Chen, Jia Rong, GangLi) 20. Mining biological interactions from biomedical

texts for efficient query answering(Muhammad Abulaish, Lipika Dey, Jahiruddin)

7. Predicting local structure and function ofproteins (Huzefa Rangwala, George Karypis)

Part II. Genomics, Transcriptomics, and Proteomics 21. Ontology-based knowledge representation ofexperiment metadata in biological data mining(Richard H. Scheuermann, Megan Kong, CarlDahlke, Jennifer Cai, Jamie Lee, Yu Qian,Burke Squires, Patrick Dunn, Jeff Wiser, HerbHagler, Barry Smith, David Karp)

8. Computational approaches for genomeassembly validation (Jeong-Hyeon Choi, HaixuTang, Sun Kim, Mihai Pop)

9. Mining patterns of epitasis in human genetics(Jason H. Moore)

10. Discovery of regulatory mechanisms from geneexpression variation by eQTL analysis (YangHuang, Jie Zheng, Teresa M. Przytycka)

22. Redescription mining and applications inbioinformatics (Naren Ramakrishnan,Mohammed J. Zaki)

11. Statistical approaches to gene expressionmicroarray data preprocessing (Megan Kong,Elizabeth McClellan, Richard H. Scheuermann,Monnie McGee)

Part V. Genome Medicine Applications23. Data mining tools and techniques for

identification of biomarkers for cancer (MickCorrell, Simon Beaulah, Robin Munro,Jonathan Sheldon, Yike Guo, Hai Hu)12. Application of feature selection and

classification to computational molecularbiology (Paola Bertolazzi, Giovanni Felici,Giuseppe Lancia)

24. Cancer biomarker prioritization: assessing thein vivo impact of in vitro models by in silicomining of microarray database, literature andgene annotation (Chia-Ju Lee, Zan Huang,Hongmei Jiang, John Crispino, Simon Lin)

13. Statistical indices for computational and datadriven class discovery in microarray data(Raffaele Giancarlo, Davide Scaturro, FilippoUtro)

25. Biomarker discovery by mining glycomic andlipidomic data (Haixu Tang, Mehmet Dalkilic,Yehia Mechref )14. Computational approaches to peptide retention

time prediction for proteomics (Xiang Zhang,Cheolhwan Oh, Catherine P. Riley, HyeyoungCho, Charles Buck)

26. Data mining chemical structures and biologicaldata (Glenn J. Myatt, Paul E. Blower)



Readership: Libraries; researchers and graduate students of bioinformatics.

“Biology has entered an informatics era” write Sael and Kihara on page 105 of this book. Inrecognition of this, Chen and Lonardi present in this book a showcase of successful recentprojects in the research area where biology, computer science, and statistics intersect.

The editors have done a good job of pulling together the work of over 80 authors into awell-typeset product with high-resolution graphics and even several diagrams of proteins. Thereare a handful of typing errors, but none that I found were disastrous. On the other hand, thereferences (an average of over 40 per paper) have not been consolidated, and some authors useauthor-date format, while some use numbers.

The authors leave no stone unturned in terms of topics and techniques. Statistical methodsemployed range from decision trees, random forests, support vector machines, cluster analysisand neural networks to 3D object analysis, Bayesian networks, graph mining and self-organizingmaps. There is a veritable alphabet soup of special software employed, with names of packagesand algorithms ranging from AAA to Zinc. Other packages named Bioprospector, Geronemo,Hawkeye, and NeMoFinder suggest there is clearly a huge amount of creativity (and movie-watching) amongst bioinformatics researchers.

For me, one of the best chapters related to my current projects came almost at the end, inChapter 23 on data mining tools for identification of biomarkers for cancer. Other readers mayfind their favourite chapters earlier on, but the one sure thing is that there is something foreveryone with an interest in bioinformatics in this book. Make sure your library has a copy, orthat you buy one for yourselves.

Alice Richardson: [email protected] of Information Sciences and Engineering

University of Canberra, ACT 2601, Australia

Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral,and Health SciencesLinda M. Collins, Stephanie T. LanzaWiley, 2010, xxxiii + 285 pages, € 76.20 / £ 63.50 / US$ 94.95, hardcoverISBN: 978-0-470-22839-5

Table of contents

Part I. Fundamentals 5. Multiple-group LCA1. General introduction 6. LCA with covariates2. The latent class model Part III. Latent Class Models for Longitudinal Data3. The relation between the latent variable and its

indicators7. RMLCA and LTA8. Multiple-group LTA and LTA with covariates

4. Parameter estimation and model selectionPart II. Advanced LCA

Readership: Statisticians and subject matter experts in categorical data in social, behavioural,and health sciences.

Latent Class Models (LCA) are easiest to introduce and understand through their continuousanalogues, namely, Factor Models, which are familiar to most statisticians. Factor Models oftenprovide a sparse representation of multivariate data. The data are observations on what arecalled manifest or observable variables like scores in tests or health conditions or something



similar. They are exhibited as linear functions of much fewer latent variables or Factors, plusan error term. If one wants to do inference, typically normality assumptions are made. Factormodels were first introduced in Psychometry, they are now widely used also in Health and SocialSciences. In addition to providing a sparse representation, the analyst may be able to identifythe Factors as explanatory variables with names like common and specific Factors. That is theultimate goal but often difficult to achieve.

Replace manifest and latent variables by corresponding categorical variables, Factors by LatentClasses, and you can see the purpose and beauty of LCA. This is how LCA is introduced by theauthors. LCA provides a sparse and meaningful representation of categorical data.

The approach in this book is based on direct modelling in terms of probabilities of themanifest and latent classes and the relations between the two, as pioneered by Goodman. Loglinear models are mentioned but not discussed.

Inference on probability of manifest classes is based on maximum likelihood estimates, which,in turn, are estimated by EM. The inverse problem is more delicate, Bayes Theorem is used. Thenumber of latent classes is determined by model selection methods.

Here is an example given right at the beginning. The data are observed proportions in sixclasses of (relatively mild) delinquent behavior of adolescents, based on data from the USNational Longitudinal Study of Adolescent Health (2003). Four explanatory latent classes arefound by the methods mentioned above. The classes are named very appropriately as non-and mild delinquency, verbal delinquency, shoplifting, and general delinquency. Compare withthe manifest classes, Lied to parents, Publicly loud/rowdy/unruly, Damaged property, Stolensomething from store, Stolen something worth < $50, Taken part in group fight, and theconditional probabilities of manifest classes given at each latent class, and you have a sense ofthe power of the method.

The book is divided into three parts. I have described Part 1 above briefly. These methods ofanalysis are extended to multi-group, data with covariates, and longitudinal data in the remainingtwo parts.

The presentation is lively and interesting. Most references come from subject matter, ratherthan statistical, journals. So this is a great chance for many of us generalists to get acquaintedwith this fascinating area. Statisticians and subject matter experts in categorical data in Social,Behavioral and Health Sciences should find the book quite useful. The data sets handled seemto be based on large samples but not high dimensional in the sense of involving a large numberof categories. Most likely they will find many useful techniques for their problems in one place.

Jayanta K. Ghosh: [email protected] of Statistics, Purdue University

West Lafayette, IN 47909, USA



Nonparametric Statistics for Non-Statisticians: A Step-by-Step ApproachGregory W. Corder, Dale I. ForemanWiley, 2009, xiii + 247 pages, € 68.40 / £ 56.95 / US$ 84.95, hardcoverISBN: 978-0-470-45461-9

Table of contents

1. Nonparametric statistics: an introduction 7. Comparing variables of ordinal or dichotomousscales: Spearman rank-order, point-biserial, andbiserial correlations

2. Testing data for normality3. Comparing two related samples: the Wilcoxon

signed ranks test 8. Tests for nominal scale data: chi-square and Fisherexact test4. Comparing two unrelated samples: the

Mann–Whitney U-test 9. Test for randomness: the runs test5. Comparing more than two related samples: the

Friedman testAppendix A. SPSS at a glanceAppendix B. Tables of critical values

6. Comparing more than two unrelated samples:the Kruskal–Wallis H-test

Readership: Students of statistics and researchers in other disciplines especially social sciences.

It is very common to come across data that do not meet the conditions for t-tests, analysis ofvariance F-tests, and the like. This book brings together non-parametric tests for data structuresranging from the two-sample test of location to tests of independence in one or two variables.

The authors made a good decision in including Chapter 2 about testing for normality, butthe reasons for using a nonparametric method are not well argued in Chapter 1. This is mainlybecause the term “parameter” is used very broadly, including the concepts of “assumption” or“condition.” I prefer a more restricted definition of parameter as a numerical characteristic of apopulation.

Chapters 3–9 follow a formula and are designed to be read quite independently. They becomequite repetitive if read one after the other, as I did, and as a result I did pick up one or twoinstances of cut-and-paste errors. Screen shots of SPSS 14 are used to explain how to implementeach test in SPSS.

A nice touch in each chapter is the section containing three or four references from the literaturein social science, education or psychology. A brief summary of each paper is provided alongwith an exhortation to find the paper if you are interested in seeing the particular nonparametricstatistic in action.

However, some turns of phrase really set me thinking and I do not think that was the intentionof this recipe-style book. Do we really approximate a large sample of data to a normal distribution(p. 5)? What is “poorly distributed data”? Is it simply non-normal? That could be quite properfor many data sets.

This book assumes that the reader has completed an introductory statistics course. Terms likeempirical frequency distribution (p. 26) are introduced without explanation. Formulae for theeffect size are given wherever relevant, but only one or two confidence interval calculations areworked out in full. The formulae for tied data are also given wherever relevant, but the power ofthe tests is not described.

Each chapter contains five or six exercises using data that is very likely to be fictitiousalthough placed in a plausible scenario, for example schools, gyms, universities. Solutions aregiven using SPSS straight after the exercises.



The usual tables for all the tests discussed are also provided. However, I found the tables to bevery badly set out. Some of them do not start at the top of the page and the table of the Normaldistribution manages to run in three columns only across half a dozen pages!

The reference list is very short, barely two pages, with very little reference to either originalresearch or the classic books on non-parametric statistics.

I have been using Daniel (1990) as the textbook for a second year non-parametrics coursefor a few years now, and I am beginning to tire of the gruesome data sets and the plain overallappearance of the book. Is this book going to be the one I was hoping for to replace it? Wellno. Students and practitioners in a variety of social sciences will appreciate its highly structuredrecipe-like approach. But the book does not contain the statistical theory to make it work on itsown for students studying statistics as a major.


University of Canberra, Bruce ACT 2601, Australia

Reference

Daniel, W.W. (1990). Applied Nonparametric Statistics, 2nd ed. Boston: PWS-Kent.

Introductory Time Series with RPaul S. P. Cowpertwait, Andrew V. MetcalfeSpringer, 2009, xvi + 254 pages, € 49.95 / £ 44.99 / US$ 59.95, softcoverISBN: 978-0-387-88697-8

Table of contents

1. Time series data 7. Non-stationary models2. Correlation 8. Long memory processes3. Forecasting strategies 9. Spectral analysis4. Basic stochastic models 10. System identification5. Regression 11. Multivariate models6. Stationary models 12. State space models

Readership: Later year undergraduates, beginning graduate students, and researchers andgraduate students in any discipline needing to explore and analyse time series data.

This very readable text covers a wide range of time series topics, always however within atheoretical framework that makes normality assumptions. The range of models that are discussedis unusually wide for an introductory text. More advanced topics can however be omitted withoutobvious loss to the remaining discussion.

The range of examples is wide—climate data, electricity, and other production data, waveheights, building approvals and activity, letters of complaint, wine sales, stock prices, and so on.The computer output is discussed in detail. The end of each chapter has a list of R commands thatwere used. Code that can be used to reproduce the analyses and simulations, and the extensivecollection of data sets, are available from the book’s web site.

The text is dotted with helpful comments that place the theory in context. Section 2.2.2 has anice discussion that contrasts the single historical time series that is typically available with “the



ensemble of all possible time series that might have been produced by the time series model.”Chapter 3, on forecasting, has a nice discussion of the Bass model for the adoption and diffusionof a new product when it comes on to the market. This chapter could usefully be supplementedwith a note on the “forecast” package for R, described in Hyndman and Khandakar (2008). Seealso the references given in that paper.

Both time and frequency domain models are considered. ARCH and GARCH models,commonly used for financial time series, are discussed in some detail. Fractionally, differencedmodels are used to handle long memory processes.

A strength is the extensive use of simulation, both to assist understanding, and to check outthe properties of fitted models. As the authors say (p. 73), “Simulation can . . . help ensure thatthe model is both correctly understood and correctly implemented.”

The mathematical theory is remarkably complete, but is secondary to motivation of themethodology and to the use of this methodology with carefully chosen practical examples. Thistext is recommended for its wide-ranging and insightful coverage of time series theory andpractice.

John H. Maindonald: [email protected] for Mathematics & Its Applications

Australian National UniversityCanberra ACT 0200, Australia

Reference

Hyndman, R.J. & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. J. Stat. Software,27(3), 1–22. Available online: http://www.jstatsoft.org/v27/i03

Basic Statistics: A Primer for the Biomedical Sciences, Fourth EditionOlive Jean Dunn, Virginia A. ClarkWiley, 2009, xiv + 255 pages, € 80.40 / £ 66.95 / US$ 99.95, hardcoverISBN: 978-0-470-24879-9

Table of contents

1. Initial steps 10. Categorical data: proportions2. Populations and samples 11. Categorical data: analysis of two-way frequency tables3. Collecting and entering data 12. Regression and correlation4. Frequency tables and their graphs 13. Nonparametric statistics5. Measures of location and variability 14. Introduction to survival analysis6. The normal distribution Appendix A. Statistical tables7. Estimation of population means:

confidence intervalsAppendix B. Answers to selected problemsAppendix C. Computer statistical program resources

8. Tests of hypotheses on population means9. Variances: estimation and tests

Readership: Students and teachers of a one-semester course in biostatistics; users of biostatisticalmethods.

A statistics textbook which has made it to the fourth edition, as is the case with this one, isclearly fulfilling a need in the statistics community. Dunn wrote the first two editions of this



text, and Clark reports in the preface to this edition the sad news that Dunn died in January 2008.The 14 chapters of varying length cover all the main topics in basic statistics, from summary

statistics and graphs through to inference for means, proportions, and regression. A final chapteron survival analysis helps to capture the biomedical market and distinguish this book from thevast choice of general-purpose introductory statistics texts. Each chapter contains half a dozenexercises, some of which are drill-type and others of a more reflective nature. Answers to selectedexercises appear in the back of the book.

Of course, a book this size (around 250 pages) cannot, and does not claim to, includeeverything. Some very basic concepts, such as formulae and calculation of the man and median,are included. But sometimes the reader is left hanging with a throwaway line such as (p. 54) “Themean and standard deviation does not tell us everything there is to know about a distribution.” Nomention of the 67-95-99.7 rule to be found, not even in the chapter on the Normal distribution.

The quality of the graphics in the book is on the low side, despite the comment on p. 2 that“computers and their accompanying graphic programs have made it possible to obtain attractiveand meaningful displays of data . . .”!

The book is not tied to any particular software, which increases its versatility, but does meanthat students will be likely to need another book on software. An appendix mentions the mainstatistical packages, along with an interesting collection of references to software reviews injournals. Another appendix contains half a dozen standard statistical tables.

In conclusion, the comfortable size of this book and its broad (if not exhaustive) coverage ofintroductory statistical concepts make it a good choice for the desks of students and users ofstatistics in the biomedical sciences.



Design and Analysis of Quality of Life Studies in Clinical Trials, Second EditionDiane L. FaircloughChapman & Hall/CRC, 2010, xx + 404 pages, £ 57.99 / US$ 89.95, hardcoverISBN: 978-1-4200-6117-8

Table of contents

1. Introduction and examples 11. Random effects dependent dropout2. Study design and protocol development 12. Selection models3. Models for longitudinal studies I 13. Multiple endpoints4. Models for longitudinal studies II 14. Composite endpoints and summary measures5. Moderation and mediation 15. Quality adjusted life-years (QALYs) and Q-TWiST6. Characterization of missing data 16. Analysis plans and reporting results7. Analysis of studies with missing data Appendix C. Cubic smoothing splines8. Simple imputation Appendix P. PAWS/SPSS notes9. Multiple imputation Appendix R. R notes

10. Pattern mixture and other mixture models Appendix S. SAS notes

Readership: Public health professionals, medical researchers.

The book begins appropriately with the raison d’etre for the concept of health-related qualityof life. It is pointed out that the most important outcome of treatment from the patient’s pointof view is commonly the perception of benefit and well being. Further, unlike more objective



measures, like blood pressure, it cannot be measured directly and so strategies for assessmentare discussed. This is followed in Chapter 1 by a description of some data sets extracted fromfive clinical trials that are to be used throughout the book to illustrate the methodology; thesedata sets are made available on a website. Details for computation, using SAS, SPSS, and R, aregiven throughout, though there are no dedicated exercises for the reader.

Two chapters are concerned with study design and conduct: Chapter 2 discusses the designof a clinical trial, including various protocol ingredients, and Chapter 16 focuses upon theplanned analysis and reporting of the results. Two chapters are devoted to statistical methodsfor repeated measures or longitudinal data: Chapter 3 covers the multivariate linear model withcertain covariance structures, and Chapter 4 extends the treatment to the linear mixed model,which has both fixed and random effects. In Chapter 5 we learn that a “moderator” determineshow a given predictor x affects a given response y, for example, x = disability, y = quality oflife, moderator = age; a “mediator” is used to explain a causal effect as x → mediator → y.

Missing data is a common feature for quality-of-life assessments in longitudinal studies.Chapters 6–12 are concerned with this problem in its various forms. Topics covered include“missing at random” and its relatives, imputation (simple and multiple), mixture models for datawith drop-outs and other missing patterns, and selection models for missing data.

The penultimate three chapters tackle multiple comparisons and testing (Chapter 13), summarymeasures (Chapter 14), and QALYs and Q-TWiST (Chapter 15). (QALYs are of particularinterest and controversy in England where they are used to ration health care.) In addition, thereare some appendices giving detailed notes for computation.

The book sits well in the Interdisciplinary Statistics Series, containing much insightfuldiscussion of the issues and not too much mathematics. It is carefully written and well organized,and likely to become a standard reference in the area, taking its place on many a bookshelf bothpersonal and library-based.


London SW7 2AZ, UK



Statistical Models and Causal Inference: A Dialogue with the Social SciencesDavid A. Freedman, Edited by David Collier, Jasjeet S. Sekhon, Philip B. StarkCambridge University Press, 2010, xvi + 399 pages, £ 22.99 / US$ 29.99, softcover (alsoavailable in hardcover)ISBN: 978-0-521-12390-7

Table of contents

Editors’ introduction: inference and shoe leather 11. Survival analysis: an epidemiological hazard?Part I. Statistical Modeling: Foundations and

LimitationsPart III. New Developments: Progress or

Regress?1. Some issues in the foundations of statistics:

probability and statistical models12. On regression adjustments in experiments with

several treatments2. Statistical assumptions as empirical

commitments13. Randomization does not justify logistic

regression3. Statistical models and shoe leather 14. The grand leap

Part II. Studies in Political Science, Public Policy, andEpidemiology

15. On specifying graphical models for causation,and the identification problem

4. Methods for Census 2000 and statisticaladjustments

16. Weighting regressions by propensityscores

5. On “solutions” to the ecological inferenceproblem

17. On the so-called “Huber sandwich estimator”and “robust standard errors”

6. Rejoinder to King 18. Endogeneity in probit response models7. Black ravens, white shoes, and case selection:

inference with categorical variables19. Diagnostics cannot have much power against

general alternatives8. What is the chance of an earthquake? Part IV. Shoe Leather Revisited9. Salt and blood pressure: conventional wisdom

reconsidered20. On types of scientific inquiry: the role of

qualitative reasoning10. The Swine Flu vaccine and Guillain-Barre

Syndrome: relative risk and specific causation

Readership: Everyone interested in the application of statistics, especially students andprofessionals in the social and health sciences.

This book should be read in conjunction with Freedman’s second major book on statistics,Statistical Models: Theory and Practice, reviewed above. Indeed, it can be seen as the completionof a trilogy, the first volume of which was Statistics, by Freedman, Pisani, and Purves, now inits 4th edition (Norton, 2007). Together these three books constitute an unequalled resource forlearning how to think about statistics, and how to use statistics to conduct serious analyses. Itis impossible to recommend them too highly. They should be on every statistician’s shelf, andconsulted regularly.

The present volume collects 20 of Freedman’s papers on applied statistics, all but onepreviously published in the years 1990 to the present, with a spike in the years 2008–2009consisting of papers completed just before to his untimely death in 2008. They were selectedfrom more than twice that number, written since the late 1980s on census adjustment, politicalscience, public policy, epidemiology, statistics and the law, the bootstrap, and procedures fortesting and evaluating models. There is much in this volume to interest readers at all levels,and an equal amount in papers by Freedman not collected here, many of which are listed inthe references in this volume. (A valuable addition to later printings of this book would be acomplete listing of Freedman’s papers.)

What’s in this book for you? The briefest summary would use the subtitle of paper 9:Conventional wisdom reconsidered. Do you think the problem of ecological regression has



been solved? Read Chapters 5 and 6. That adjustment to the census results necessarily improvesthem, or that regression adjustments to experimental data cannot make things worse? SeeChapters 4 and 12. That statisticians have now figured out how to make causal inferences fromnon-experimental data? Chapters 14 and 15. That regression diagnostics really can be trusted tocheck the basic assumptions of our model? Chapter 19. You get the idea. As for the conventionalwisdom on the relationship between salt and blood pressure, read paper 9, and at the same time,why not read the rest of the book?

Terry Speed: [email protected] of Statistics, 367 Evans Hall #3860

University of California, Berkeley, CA 94720-3860, USA

Statistical Models: Theory and Practice, Revised EditionDavid A. FreedmanCambridge University Press, 2009, xiv + 442 pages, £ 24.99 / US$ 40.00, softcoverISBN: 978-0-521-74385-3

Table of contents

1. Observational studies and experiments 7. Maximum likelihood2. The regression line 8. The bootstrap3. Matrix algebra 9. Simultaneous equations4. Multiple regression 10. Issues in statistical modeling5. Multiple regression: special topics Appendix: Sample MATLAB code6. Path models

Readership: Primarily advanced undergraduates or beginning graduate students in statistics.Secondarily, everyone else interested in the application of statistics, especially students andprofessionals in the social and health sciences.

In the 1980s David Freedman began teaching an introductory graduate course in applied statisticsat UC Berkeley. His course was like no other, and forever changed the lives of many whoparticipated in it, including mine. It was built around a very large reader of original articlesand excerpts from books, including one entire book: John Snow’s 1855 classic On the mode ofcommunication of cholera. Classes were discussions of material from the reader, interspersedwith blackboard demonstrations of relevant theory by a student or the teacher. There was a lotof hard thinking during those sessions, and a lot of silence. At times, the atmosphere was tense;very serious matters were being discussed: does statistical modelling deliver on its promises?Many eyes were opened, to good uses and bad abuses of statistics, to assumptions and theirimplications, to emperors with no clothes on, to the power of shoe leather, in short, to the good,the bad and the ugly of our profession. There were also labs, typically involving re-analysis ofthe original data from the articles, or relevant simulations, and the answering of tough questions.As time went on, the short pieces on theory Freedman was constantly writing, and exercisesaccumulated. By 2005 the original edition of this book appeared. This shared with the rest of theworld the unique opportunity available at Berkeley of learning statistics from a master, one whowrote clearly, critically, and with humour. It was widely and justifiably acclaimed. The presentrevised edition follows the original one, but with many corrections and small improvements.

What’s new and different about this book? The short answer is: almost everything. For astart, you will find exceptionally lucid and insightful explanations of standard methods used



by every applied statistician, such as multiple regression and maximum likelihood. Secondly,it has a very large collection of wonderful exercises. But we have hardly begun. The bookcontains something no other book contains: extended discussions of theory and case studiesclosely intertwined. Once you have the basics of the theory of simultaneous equations, asocial-science example on education and fertility from the American Sociological Reviewis thoroughly dissected. Assumptions about errors, omitted variables, additive linear effects,constant coefficients, exogeneity, identifiability, and the structural nature of the equations, areexplained and examined, the models interpreted and questioned, as sociology and as statistics.This is done for four major case studies reprinted at the end, and for many smaller ones describedbriefly through the book. This is the book for every aspiring applied statistician. It is not abouttechniques, but a way of thinking. A companion was reviewed above.



Multivariate Statistics: High-Dimensional and Large-Sample ApproximationsYasunori Fujikoshi, Vladimir V. Ulyanov, Ryoichi ShimizuWiley, 2010, xviii + 533 pages, € 92.40 / £ 76.95 / US$ 115.00, hardcoverISBN: 978-0-470-41169-8

Table of contents

1. Multivariate normal and related distributions 11. Canonical correlation analysis2. Wishart distributions 12. Growth curve analysis3. Hotelling’s T2 and Lambda-statistics 13. Approximation to the scale-mixed distributions4. Correlation coefficients 14. Approximation to some related distributions5. Asymptotic expansions for multivariate basic

statistics15. Error bounds for approximations of

multivariate tests6. MANOVA models 16. Error bounds for approximations to some other

tests7. Multivariate regression8. Classical and high-dimensional tests for

covariance matricesA.1. Some results on matricesA.2. Inequalities and max-min problems

9. Discriminant analysis A.3. Jacobians of transformations10. Principal component analysis

Readership: Both practical and theoretical statisticians as well as graduate students.

As the authors state in the Preface, “In the last years we encounter more and more problems inapplications when p (number of variables) is comparable with n (number of observations) or evenexceeds it. Some examples of high-dimensional data include curve data, spectra, images, andDNA micro-arrays. Therefore, it becomes essential to revise the classical multivariate methods inorder to make them useful in wide range of relations between p and n and to extend multivariatestatistical theory in high-dimensional situations.”

The above motivation has put the authors into a demanding job in which they have succeededvery nicely, and earn congratulations for valuable work. The book proceeds in a pretty reader-friendly style, introducing the required concepts in a peaceful pace, and hence, the book willeasily find its place in the classrooms, not only in the researchers’ desks. Parenthetically, I thinkthis is the first book I have seen where the column vectors, random vectors, n-by-m matrices withreal elements and n-by-m matrices with random elements are separated by different notations



(the secret being the slanted boldface): not bad idea. There is no author index but I appreciatethe page references in each item of the bibliography. I am inclined to believe that the authors’last sentence in the Preface, “We believe and hope that the book will be useful in the futuredevelopments in multivariate analysis,” will be fulfilled.

Simo Puntanen: [email protected] of Mathematics and StatisticsFI-33014 University of Tampere, Finland

Spatial Statistics and ModelingCarlo Gaetan, Xavier GuyonSpringer, 2010, xv + 297 pages, € 79.95 / £ 72.00 / US$ 89.95, hardcoverISBN: 978-0-387-92256-0

Table of contents

1. Second-order spatial models and geostatistics A. Simulation of random variables2. Gibbs-Markov random fields on networks B. Limit theorems for random fields3. Spatial point processes C. Minimum contrast estimation4. Simulation of spatial models D. Software5. Statistics for spatial models

Readership: Students and researchers in statistics, geology, image processing, spatial economics,earth sciences, epidemiology, and other areas.

Spatial statistics is an exciting and rapidly emerging branch of statistics and is applied in awide range of fields. Although a rapidly emerging area in science demands a steady stream oftextbooks to meet the requirements of a large number of researchers interested in that area, it alsoposes a challenge to potential authors in selecting the topics to cover in a new text. In that sense,the authors of the current book do an excellent job in selecting what I consider to be the mostrelevant topics for a new investigator just venturing into this exciting area. This book primarilydeals with three broad areas of spatial statistics: Geostatistics, Spatial Statistics on NetworkData, and Point Data. The authors seem to have struck the right balance between the rigoroustheoretical models behind the statistical methods and providing relevant examples in full detail.In each of the topics, before presenting the theory, the authors provide great motivation forthe subject by providing pertinent and accessible examples. Specifically, the authors presentedagriculture data for spatial regression, climate data for kriging, simulated data for random fieldson networks, forest data in point processes among many other real and simulated examples. Forthe benefit of the readers, the authors have also provided the data and “R” scripts in a dedicatedwebsite accompanying the book.

I personally found this book to be well structured, accessible, and easy to read withoutcompromising the theoretical rigor of the subject. Although the book does not specificallydiscuss applications of spatial statistics in brain imaging (my research interest), I found mostof the topics to be quite relevant. Any researcher interested in statistical methodologies forbrain imaging will find the book quite engaging. This book will have a permanent place in mybookshelf.

Rajesh Ranjan Nandy: [email protected] of Biostatistics & Psychology

University of California, Los Angeles, CA 90095, USA



Regression Estimators: A Comparative Study, Second EditionMarvin H. J. GruberJohns Hopkins University Press, 2010, xii + 412 pages, US$ 110.00, hardcoverISBN: 978-0-8018-9426-8

Table of contents

Part I. Introduction and Mathematical Preliminaries 8. The MSE for incorrect prior assumptions1. Introduction Part IV. Applications2. Mathematical and statistical preliminaries 9. The Kalman filter

Part II. The Estimators, Their Derivations, and TheirRelationships

10. Experimental design models11. How penalized splines and ridge-type

estimators are related3. The estimators4. How the different estimators are related Part V. Alternative Measures of Efficiency

Part III. Comparing the Efficiency of the Estimators 12. Estimation using Zellner’s balanced lossfunction5. Measures of efficiency of the estimators

6. The average mean square error 13. The LINEX and other asymmetric lossfunctions7. The MSE neglecting the prior assumptions

14. Distances between ridge-type estimators, andinformation geometry

Readership: All interested in Ridge Regression and subsequent developments of it.

This is a very specialized book dealing with various ridge-regression-type estimation techniquesused with linear models. Ridge regression [least squares estimation with an added ellipsoidalrestriction added to the parameter space] was introduced by Hoerl and Kennard, who wrote aseries of papers beginning in the 1970s. Gruber’s first edition appeared in 1990; this secondedition is extensively updated. It is extremely well written by an expert in the area. Many of the150 exercises are expansions of points in the text; this offers great encouragement to the readerto gain more insight into what has been read. Conversely, however, a criticism is that there isvery little “real” data given, in spite of the fact that improved data analysis was the original pointof using the techniques. A reader or an instructor who is prepared to supply this missing aspectwill enjoy this book even more. It is excellent.

Norman R. Draper: [email protected] of Statistics, University of Wisconsin–Madison

1300 University Avenue, Madison, WI 53706-1532, USA



Theory of Stochastic Processes: With Applications to Financial Mathematics and RiskTheoryDmytro Gusak, Alexander Kukush, Alexey Kulik, Yuliya Mishura, Andrey PilipenkoSpringer, 2010, xii + 375 pages, € 59.95 / £ 49.99 / US$ 69.95, hardcoverISBN: 978-0-387-87861-4

Table of contents

1. Definition of stochastic process. Cylinderσ -algebra, finite-dimensional distributions, theKolmogorov theorem

11. Renewal theory. Queueing theory12. Markov and diffusion processes13. Ito stochastic integral. Ito formula. Tanaka

formula2. Characteristics of a stochastic process. Meanand covariance functions. Characteristicfunctions

14. Stochastic differential equations15. Optimal stopping of random sequences and

processes3. Trajectories. Modifications. Filtrations4. Continuity. Differentiability. Integrability 16. Measures in a functional spaces. Weak

convergence, probability metrics. Functional limittheorems

5. Stochastic processes with independentincrements. Wiener and Poisson processes.Poisson point measures 17. Statistics of stochastic processes

6. Gaussian processes 18. Stochastic processes in financial mathematics(discrete time)7. Martingales and related processes in discrete

and continuous time. Stopping times 19. Stochastic processes in financial mathematics(continuous time)8. Stationary discrete- and continuous-time

processes. Stochastic integral over measurewith orthogonal values

20. Basic functionals of the risk theory

9. Prediction and interpolation10. Markov chains: discrete and continuous time

Readership: Advanced undergraduates and postgraduates in mathematics, and teaching staff atthese levels.

This is a book in the Springer series on Problem Books in Mathematics, presenting a seriesof problems, rather than a classical didactic text. Each of the 20 chapters in this book has acondensed outline of the topic being considered, a bibliography, the problems, and then hints orsolutions to most of the problems. Assumed background knowledge includes probability theory,calculus, and measure theory. Some of the problems will be familiar from other collectionsof probability problems, but some are original. The level of the book is comparable with, butperhaps slightly higher than, Grimmet and Stirzaker (1992) Probability and Random Processes:Problems and Solutions, though Grimmet and Stirzaker is not one of the other collections ofprobability problems listed in the bibliography.


London SW7 2AZ, UK



Design and Analysis of Vaccine StudiesM. Elisabeth Halloran, Ira M. Longini, Jr., Claudio J. StruchinerSpringer, 2010, xviii + 387 pages, € 79.95 / £ 72.00 / US$ 89.95, hardcoverISBN: 978-0-387-40313-7

Table of contents

1. Introduction and examples 9. Vaccine effects on post-infection outcome2. Overview of vaccine effects and study designs 10. Household-based studies3. Immunology and early phase trials 11. Analysis of households in communities4. Binomial and stochastic transmission models 12. Analysis of independent households5. R0 and deterministic models 13. Assessing indirect, total, and overall effects6. Evaluating protective effects of vaccination 14. Randomization and baseline transmission7. Modes of action and time-varying VES 15. Surrogates of protection8. Further evaluation of protective effects

Readership: Researchers and postgraduate students in infectious disease epidemiology andbiostatistics.

This book lays out a framework for planning and analysing studies on vaccine evaluation. Thecomplex nature of “dependent happenings,” that is, the fact that the chance of infection foran individual ultimately depends on the infection status of others in the population, provides awealth of possibilities, and associated difficulties, in understanding and analysing the impact ofvaccination. The book covers topics on different aspects of vaccine evaluation, including effecton the individual and population levels, as well as introductory material to the basic concepts ofstatistical analysis of infectious disease data in general.

The technical level of presentation is kept such that the various concepts and methods are firstexplained in an elementary way in each chapter, useful to every practicing biostatistician andepidemiologist in the field. More complex examples and generalizations are then given in termsof either examples reviewed from literature or comprehensive references to research literature.In general, the book is based on an extensive literature review and thus contains a large numberof references to research in the field up to the present. At the end of each chapter, there are anumber of exercises which serve as a kind of checklist of the main topics.

The focus in this book is on estimation rather than on hypothesis testing, in agreementwith the modern standards in epidemiology. Much and highly appropriate emphasis is givento the coherent choice and interpretation of various effect measures for vaccine efficacy andeffectiveness of vaccination and their estimators under different designs. The designs cover bothexperimental and observational situations. Various concepts, for example, the different effects ofvaccination on an individual versus on the population, or the effects on susceptibility to infectionversus on pathogenicity, are discussed in a unified framework developed by the authors in theirprevious research.

In summary, the authors have succeeded in writing an impressive overview of a complex topic.In my opinion the book belongs to the shelf of all those interested in the analysis of infectiousdisease data. More generally, the book serves a useful textbook of statistics applied to real-lifeproblems in which the balance between substantive research questions and the availability ofdata always remains an issue.

Kari Auranen: [email protected] of Vaccination and Immune Protection, National Institute for Health and Welfare

P.O. Box 30, FI-00271 Helsinki, Finland



Applied Survey Data AnalysisSteven G. Heeringa, Brady T. West, Patricia A. BerglundChapman & Hall/CRC, 2010, xix + 467 pages, £ 49.99 / US$ 79.95, hardcoverISBN: 978-1-4200-8066-7

Table of contents

1. Applied survey data analysis: overview 8. Logistic regression and generalized linearmodels for binary survey variables2. Getting to know the complex sample design

3. Foundations and techniques for design-basedestimation and inference

9. Generalized linear models for multinomial,ordinal, and count variables

4. Preparation for complex sample survey dataanalysis

10. Survival analysis of event history surveydata

5. Descriptive analysis for continuous variables 11. Multiple imputation: methods and applicationsfor survey analysts6. Categorical data analysis

7. Linear regression models 12. Advanced topics in the analysis of survey dataAppendix: Software overview

Readership: Survey practitioners in various fields (social sciences, government, public health,and others).

The book arises out of consultancy and courses on survey research methodology conducted bythe authors. Some basic knowledge of applied statistics is assumed, roughly in line with whata social scientist, for example, would typically be exposed to in their undergraduate degreecourse. The equation-count is quite low compared with many books on Statistics: the preferenceis for practical numerical demonstration, listing the required computer code, along with plentyof discussion of the detailed examples examined.

A review of the history of the subject plus some basic guidance for good practice is set out in thefirst chapter. Survey design, including simple random sampling, stratification, clustering, multi-stage sampling and weighting, are covered in Chapter 2. Inference is the focus in Chapters 3 and4: finite populations, superpopulation models, estimation, testing, bias, and the overall conductof analysis. Chapters 5 and 6 consider simple analyses of continuous responses (univariate andbivariate) and categorical variables (proportions and contingency tables). Regression (linear,binary, multinomial, and count data) is dealt with in Chapters 7–9. Subsequently, some morespecialized areas are surveyed: survival analysis (Chapter 10), imputation for missing data(Chapter 11), and a variety of topics in Chapter 12 (Bayesian analysis, generalized linear mixedmodels, structural equation models, small area estimation, and non-parametric methods). As theauthors point out, these are not subjects that commonly appear in treatments of survey sampling.

Appendix A gives an extensive guide to available computer packages, Stata, SAS, SPSS, andSUDAAN in particular. In addition, there is a website containing supplementary material, codefor the examples used in the book, and links for accessing recent publications, data sources, etc.Also, 7 of the 12 chapters have exercises for the reader at the end.

As can be seen from the above summary, there is a wealth of instruction here. The writingstyle is expansive, keeping mathematics in check, and the material is well organized clearly intoappropriate sections. I think that the book would serve any budding survey practitioner well:armed with the knowledge and practical skills covered herein, plus some real-life experience ofcourse, one could reasonably claim to be well qualified in the subject.


London SW7 2AZ, UK



Bayesian Analysis for the Social SciencesSimon JackmanWiley, 2009, xxxiv + 564 pages, £ 45.00 / € 54.00 / US$ 90.00, hardcoverISBN: 978-0-470-01154-6

Table of contents

Introduction Part III: Advanced Applications in the SocialSciencesPart I: Introducing Bayesian Analysis

1. The foundations of Bayesian inference 7. Hierarchical statistical models2. Getting started: Bayesian analysis for simple

models8. Bayesian analysis of choice making9. Bayesian approaches to measurement

Part II: Simulation Based Bayesian Analysis Part IV: Appendices3. Monte Carlo methods A. Working with vectors and matrices4. Markov chains B. Probability review5. Markov chain Monte Carlo C. Proofs of selected propositions6. Implementing Markov chain Monte Carlo

Readership: Advanced graduate students and researchers in applied sciences with strongquantitative skills who are interested in learning and applying Bayesian methods in their research.

The author provides a rigorous development of Bayesian methods for analysis of data in thesocial sciences. In the first part of the book, the author presents many situations in support ofthe Bayesian paradigm over the classical counterpart. This approach is particularly importantto present to social scientists, the targeted readership, if indeed it fits with their scientificsettings as the author indicates. Whatever one’s philosophy, there is no question that the bookhas done an excellent job in covering a Bayesian perspective with carefully detailed whys andhows of implementing it, without losing technical rigour, keeping application on the forefrontwith many real-life examples. The extensive coverage of MCMC methods is commendable.Detailed Bayesian analysis for many of the examples, with consideration to the choice of priorsand assessing the sensitivity of results for those choices helps to complete the Bayesian picture.Availability of BUGS/JAGS and R codes is invaluable for readers who are looking for immediateapplication. Most of the real-life examples relate to voting and election results, naturally fittingthe author’s mark as a political scientist. The wide coverage of situations where outcomes arenot only continuous but also binary, count, longitudinal, etc. makes this a comprehensive bookfor the applied scientist. While the appeal is strongly applied, the myriad of topics covered in thisbook are quite technical. So it is not for the casual reader but rather for one who has a seriousinterest in learning to apply Bayesian methods and has fairly strong quantitative skills.

Karabi Nandy: [email protected] of Nursing and Department of Biostatistics

University of California, Los Angeles, CA 90095, USA



Sample Sizes for Clinical TrialsSteven A. JuliousChapman & Hall/CRC, 2009, xxviii + 299 pages, £ 48.99 / US$ 79.95, hardcoverISBN: 978-1-58488-739-3

Table of contents

1. Introduction 9. Sample size calculations for parallel groupsuperiority clinical trials with binary data2. Seven key steps to cook up a sample size

3. Sample sizes for parallel group superiority trialswith normal data

10. Sample size calculations for superioritycross-over clinical trials with binary data

4. Sample size calculations for superioritycross-over trials with normal data

11. Sample size calculations for non-inferiority trialswith binary data

5. Sample size calculations for equivalence clinicaltrials with normal data

12. Sample size calculations for equivalence trialswith binary data

6. Sample size calculations for non-inferiorityclinical trials with normal data

13. Sample size calculations for precision-based trialswith binary data

7. Sample size calculations for bioequivalencetrials

14. Sample size calculations for clinical trials withordinal data

8. Sample size calculations for precision-basedclinical trials with normal data

15. Sample size calculations for clinical trials withsurvival data

Readership: Researchers undertaking clinical research in the pharmaceutical and public sector.

Sample size calculation is a task that every person undertaking clinical research in thepharmaceutical or public sector will meet sooner or later. These researchers are also the intendedaudience of this book. For a book such as this, it is unavoidable to have a lot of formulas. Theseare, however, supplemented with many worked examples based on real-world issues, as well ascomprehensive tables. The theoretical background and derivation of the sample size formulasare also discussed.

The book should be useful as a reference work for statisticians or other researchers that areinterested in quickly finding an appropriate formula for a sample size calculation problem.Although the worked examples may be quite short, they are useful for understanding thediscussed methods and for the reader to apply the methods by himself or herself. Each chapteralso ends with a valuable short summary that gives the key messages to be learned from thechapter in question.





Statistical Modelling and Regression Structures: Festschrift in Honour of LudwigFahrmeirThomas Kneib, Gerhard Tutz (Editors)Physica-Verlag, 2010, xvii + 472 pages, € 79.95 / £ 72.00 / US$ 89.95, hardcoverISBN: 978-3-7908-2412-4

Table of contents

The smooth complex logarithm and quasi-periodicmodels (Paul H. C. Eilers)

P-spline varying coefficient models for complex data(Brian D. Marx)

Penalized splines, mixed models and Bayesian ideas(Goran Kauermann)

Bayesian linear regression—different conjugatemodels and their (in)sensitivity to prior-dataconflict (Gero Walter, Thomas Augustin)

An efficient model averaging procedure for logisticregression models using a Bayesian estimator withLaplace prior (Christian Heumann, Moritz Grenke)

Posterior and cross-validatory predictive checks: Acomparison of MCMC and INLA (Leonhard Held,Birgit Schrodle, Havard Rue)

Data augmentation and MCMC for binary andmultinomial logit models (SylviaFruhwirth-Schnatter, Rudolf Fruhwirth)

Generalized semiparametric regression withcovariates measured with error (Thomas Kneib,Andreas Brezger, Ciprian M. Crainiceanu)

Determinants of the socioeconomic and spatialpattern of undernutrition by sex in India: Ageoadditive semi-parametric regression approach(Christiane Belitz, Judith Hubner, Stephan Klasen,Stefan Lang)

Boosting for estimating spatially structured additivemodels (Nikolay Robinzonov, Torsten Hothorn)

Generalized linear mixed models based on boosting(Gerhard Tutz, Andreas Groll)

Measurement and predictors of a negative attitudetowards statistics among LMU students (CarolinStrobl, Christian Dittrich, Christian Seiler, SandraHackensperger, Friedrich Leisch)

Graphical chain models and their application (IrisPigeot, Stephan Klasen, Ronja Foraita)

Indirect comparison of interaction graphs (UlrichMansmann, Markus Schmidberger, Ralf Strobl,Vindi Jurinovic)

Modelling, estimation and visualization ofmultivariate dependence for high-frequency data(Erik Brodin, Claudia Kluppelberg)

Ordinal- and continuous-response stochasticvolatility models for price changes: An empiricalcomparison (Claudia Czado, Gernot Muller,Thi-Ngoc-Giau Nguyen)

Copula choice with factor credit portfolio models(Alfred Hamerle, Kilian Plank)

Penalized estimation for integer autoregressivemodels (Konstantinos Fokianos)

Bayesian inference for a periodic stochastic volatilitymodel of intraday electricity prices (MichaelStanley Smith)

Online change-point detection in categorical timeseries (Michael Huhle)

Multiple linear panel regression with multiplicativerandom noise (Hans Schneeweiβ, Gerd Ronning)

A note on using multiple singular valuedecompositions to cluster complex intracellularcalcium ion signals (Josue G. Martinez, Jianhua Z.Huang, Raymond J. Carroll)

On the self-regularization property of the EMalgorithm for Poisson inverse problems (AxelMunk, Mihaela Pricop)

Sequential design of computer experiments forconstrained optimization (Brian J. Williams,Thomas J. Santner, William I. Notz, Jeffrey S.Lehman)

Readership: Researchers in statistics, biostatistics, econometrics, academic researchers interested in statisticalmodelling.

The contributions collected in this book span a wide range of modern Statistics, including generalized linearmodels, semiparametric and geoadditive regression, Bayesian inference in complex regression models, timeseries modelling, statistical regularization, graphical models, and stochastic volatility models. The list ofcontributors reflects Professor Fahrmeir’s high personal and professional appreciation in statistical community.The Festschrift to celebrate Professor Fahrmeir’s 65th birthday is a valuable collection of statistical researchpapers of high quality.

Erkki P. Liski: [email protected] of Mathematics and StatisticsFI-33014 University of Tampere, Finland



Modeling and Analysis of Stochastic Systems, Second EditionVidyadhar G. KulkarniChapman & Hall/CRC, 2010, xxi + 542 pages, £ 63.99 / US$ 99.95, hardcoverISBN: 978-1-4398-0875-7

Table of contents

1. Introduction Appendix A. Probability of events2. Discrete-time Markov chains: transient

behaviorAppendix B. Univariate random variablesAppendix C. Multivariate random variables

3. Discrete-time Markov chains: first passagetimes

Appendix D. Generating functionsAppendix E. Laplace–Stieltjes transforms

4. Discrete-time Markov chains: limiting behavior Appendix F. Laplace transforms5. Poisson processes Appendix G. Modes of convergence6. Continuous-time Markov chains Appendix H. Results from analysis7. Queueing models Appendix I. Difference and differential equations8. Renewal processes Answers to selected problems9. Markov regenerative processes

10. Diffusion processes

Readership: Undergraduates in statistics, mathematics, operations research, and related areas.

This book is designed for two successive courses in stochastic models, with the first basedon the first six chapters, and the second the last four chapters. It presents traditional materialfor introductory courses in stochastic processes and applied probability. (I have to confess to asmall tinge of disappointment that I did not find anything startlingly unusual or innovatory in thebook—but this does not detract from its strong merits as a course text, and may even strengthen itin this regard.) It assumes knowledge of calculus and matrix algebra, but does not require measuretheory. There are exercises after each chapter, generally divided into three classes: modelling,computational, and conceptual. Answers are given “to selected problems,” these being most ofthe odd-numbered problems. The depth is indicated by the fact that the “second course,” the moreadvanced last four chapters, cover queues, renewal processes, Markov regenerative processes,and diffusion processes, with the last of these including stochastic calculus, martingales, and abrief discussion of financial applications.

Changes from the first edition include removal of an appendix on stochastic ordering, addingof appendices on analysis and on differential and difference equations, a new chapter on diffusionprocesses, deletion of discussion of numerical methods and some details of Markov renewaltheory, as well as changes to the sequence of presentation of some topics. Some exercises havebeen deleted and new ones added.

The preface ends with the mark of an enthusiast: the author says “It is my fond hope thatthe students will see a stochastic model lurking in every corner of their world as a result ofstudying this book.” That enthusiasm has translated into an accessible, well paced, and verynicely presented book. The publishers are also to be commended on its nice production: it is thesort of book which is a pleasure to read. In all, it is an excellent textbook for use in introductorycourses on stochastic processes.


London SW7 2AZ, UK



The Alberta High School Mathematics Competitions 1957–2006: A Canadian ProblemBookAndy Liu (Editor)Mathematical Association of America, 2009, ix + 283 pages, US$ 61.50, hardcoverISBN: 978-0-88385-830-1

Table of contents

Introduction SolutionsAncient period: 1957–1966 Winners: 1983–2006Medieval period: 1967–1983 About the authorModern period: 1983–2006

Readership: High school and university students, teachers, and academics in areas of mathe-matics, statistics, science, and education.

The book is presented by Andy Liu who is well known internationally in problem-solving circles,and has served as Editor of the problem section of Math Horizons for a number of years and asEditor or Co-editor of several books. Professor Liu has achieved a number of awards, includingas latest the 2010 PIMS Education Prize; for info, visit http://www.pims.math.ca/news/andy-liu-university-alberta-awarded-2010-pims-education-prize

Started in 1957, the Alberta High School Mathematics Competition is the oldest Canadianmathematical contest at the provincial level. This book is part of the celebration of theCompetition’s 50th anniversary. It puts its problems and solutions into three periods: 1957–1966(Ancient), 1967–1983 (Medieval) and 1984–2006 (Modern), reflecting the changes in highschool maths curricula. The first two periods are basically of historical interest. The Modernone produces many innovative and challenging problems.

The Introduction provides a brief coverage on the Competition’s development, sponsors, boardmembers, students and teachers involved, with a special mention of the 1995 IMO. A list ofwinners is included at the end of the book. All the problems have been well selected for thecompetition and most are given an answer in the book, though only those from the Modernperiod are provided full solutions: many of them are given, after annotations or notes, multiplesolutions or approaches or arguments, even from different students with their school named. Awide range of topics for the problems is considered, including number, geometry, equation andinequality problems at various levels, and even game or chance problems relevant to classicalprobability. In the Modern period, the problems are a mixture of multiple choice questions (20questions per year in 1983–1987 and 16 questions per year in 1988–2006, First Round) andshort answer problems (5 problems per year, Second Round).

The book is the second volume in the Canadian Collection; however, it is indeed internationallyvery valuable to high school and university students, teachers, and academics in areas ofmathematics, statistics, science, and education, especially those interested in research andtraining involved in Maths Competitions.

Shuangzhe Liu: [email protected] of Information Sciences and Engineering




Introducing Statistics: A Graphic GuideEileen Magnello, Borin Van LoonIcon Books, 2009, 176 pages, £ 6.99 / US$ 9.95, softcoverISBN: 978-1-84831-056-8

Readership: Anyone either interested in or afraid of statistics.

I found this book by chance. My introductory course in statistics for social and other sciencesstarted last week, and I happened to be a bit early, so I made a short visit to a bookshop nearby.After browsing the shelves more or less randomly for a few minutes I found this book, andimmediately decided to buy it. During the last few days the book has been both amusing anduseful company.

I really appreciate the concise style and historical expertise of the author. At the same time Ienjoy the surreal but nice strips of the illustrator. There are lots of details that I will certainlymention during my course.

When I entered the lecture hall (just 5 minutes after buying the book), I instantly addedit on the course home page, and I surely recommend (and will recommend and already haverecommended!) it to my students.

Kimmo Vehkalahti: [email protected] of Social Research, StatisticsFI-00014 University of Helsinki, Finland



Matrix Partial Orders, Shorted Operators and ApplicationsSujit Kumar Mitra, P. Bhimasankaram, Saroj B. MalikWorld Scientific, 2010, xvii + 446 pages, £ 68.00 / US$ 99.00, hardcoverISBN: 978-981-283-844-5

Table of contents

1. Introduction 10. Schur complements and shorted operators2. Matrix decompositions and generalized inverses 11. Shorted operators—other approaches3. The minus order 12. Lattice properties of partial orders4. The sharp order 13. Partial orders of modified matrices5. The star order 14. Equivalence relations on generalized and outer

inverses6. One-sided orders7. Unified theory of matrix partial orders through

generalized inverses15. Applications16. Some open problems

8. The Lowner order Appendix: Relations and partial orders9. Parallel sums

Readership: Graduate students in mathematics, researchers in mathematics, statistics, andelectrical engineering.

It was delightful to learn about the existence of the book under this title and in particular, tonotice the name of the first author: Sujit Kumar Mitra, the great Indian Master of Row andColumn Spaces. Sujit Kumar Mitra was born on 23 January 1932 in Calcutta, India, and hepassed away at his home in New Delhi on 18 March 2004. Professor Mitra’s main interestswere statistical methodology, multivariate analysis, design of experiments, and matrix algebra,particularly concerning the concept of a generalized inverse of matrix. All these topics are thecore of the book.

The book provides an excellent collection of results in the areas of matrix orders and shortedoperators that are scattered in various journals. The book can be highly recommended to anyoneinterested in this field. It is not, however, necessary a picnic to browse through the pages: thisis pretty tough and terse reading, and more appropriate for advanced courses. According to thePreface, “many new results have evolved and virtually every chapter in the monograph containsresults unpublished hitherto.”

A couple of minor remarks: a more careful typing would have made subscripts and superscriptsin non-bold as they should be, and produced a consistent style in bibliography. An author indexwould have been also welcome. In any event, I am very happy to have this book in my bookshelf.

Simo Puntanen: [email protected] of Mathematics and StatisticsFI-33014 University of Tampere, Finland



The Science of Bradley Efron: Selected PapersCarl N. Morris, Robert Tibshirani (Editors)Springer, 2008, xxvi + 502 pages, € 119.95 / £ 108.00 / US$ 149.00, hardcoverISBN: 978-0-387-75691-2

Table of contents

1. From 1965: The convex hull of a random set ofpoints. Introduced by Tom Cover

12. From 1983: Estimating the error rate of aprediction rule: improvement oncross-validation. Introduced by Trevor Hastie2. From 1971: Forcing a sequential experiment to

be balanced. Introduced by Herman Chernoff 13. From 1986: Why isn’t everyone a Bayesian?Introduced by Larry Wasserman3. From 1975: Defining the curvature of a

statistical problem (with applications to secondorder efficiency). Introduced by Rob Kass andPaul Vos

14. From 1987: Better bootstrap confidenceintervals. Introduced by PeterBickel

4. From 1975: Data analysis using Stein’sestimator and its generalizations (with CarlMorris). Introduced by John Rolph

15. From 1993: An introduction to the bootstrap(with Robert Tibshirani) [excerpt]. Introducedby Rudy Beran

5. From 1976: Estimating the number of unseenspecies: how many words did Shakespeareknow? (with Ronald Thisted). Introduced byPeter McCullagh

16. From 1996: Using specially designedexponential families for density estimation(with Robert Tibshirani). Introduced by NancyReid

6. From 1977: The efficiency of Cox’s likelihoodfunction for censored data. Introduced by JohnKalbfleisch

17. From 1996: Bootstrap confidence levels forphylogenetic trees (correction) (with ElizabethHalloran and Susan Holmes). Introduced byJoe Felsenstein7. From 1977: Stein’s paradox in statistics (with

Carl Morris). Introduced by Jim Berger 18. From 1998: R. A. Fisher in the 21st century.Introduced by Stephen Stigler8. From 1978: Assessing the accuracy of the

maximum likelihood estimator: observedversus expected Fisher information (with DavidV. Hinkley). Introduced by Thomas DiCiccio

19. From 2001: Empirical Bayes analysis of amicroarray experiment (with Robert Tibshirani,John D. Storey and Virginia Tusher).Introduced by Rafael Irizarry9. From 1979: Bootstrap methods: another look at

the jackknife. Introduced by David Hinkley 20. From 2004: Least angle regression (withTrevor Hastie, Iain Johnstone and RobertTibshirani). Introduced by David Madigan

10. From 1981: The jackknife estimate of variance(with Charles Stein). Introduced by Jun Shaoand C. F. Jeff Wu 21. From 2004: Large-scale simultaneous

hypothesis testing: the choice of a nullhypothesis. Introduced by Michael Newton

11. From 1982: The jackknife, the bootstrap andother resampling plans [excerpt]. Introduced byPeter Hall President’s corner by Bradley Efron (AMSTAT News,

April 2004): “But what do statisticians do?”

Readership: All interested in statistical research.

It is hard to imagine someone interested in statistical research who is not familiar with thebootstrap in one form or another, and hence with the name Bradley Efron. How did the bootstraparise? How did the statistician Efron arise? What other research did Efron conduct? What do hispeers think of his research? What do his colleagues and students think of him? What does helook like? What does he think about statistics? If you are interested in partial answers to any ofthe above questions, this is a book for you. It also contains reprints of 19 of Efron’s best papers,excerpts from two of his books, some biographical material, his list of publications to 2007, andthe answer to the question “What do statisticians do?”

A glance at the contents of this book reveals that the papers cover a very wide variety of topicsfrom statistics. Some are easy reading, such as “Why isn’t everyone a Bayesian?” Others requirevery close attention, such as “Defining the curvature of a statistical problem.” The first of thesepapers is from the The American Statistician, and the second is from The Annals of Statistics, so



my assessment of their level of difficulty should come as no surprise. Others in the collection arefrom Biometrika, JASA, Statistical Science, Scientific American, and AMSTAT News, so there issomething for everyone. Every paper is preceded by a commentary by an expert on the subjectof the paper, and this doubles the value of the collection. In most cases, the commentaries areeasier reading than the papers, but more importantly, they should encourage you to read thepapers, for they help you get a sense of the importance of Efron’s work, and where it fits intothe broader scheme of things. Two of Efron’s books are masterfully reviewed, and a number ofthe papers are followed by discussions, and Efron’s rejoinder.

This is a treasure trove of statistical gems, and deserves a place on the shelf of every researchstatistician.



Generalized Linear Models: with Applications in Engineering and the Sciences, SecondEditionRaymond H. Myers, Douglas C. Montgomery, G. Geoffrey Vining, Timothy J. RobinsonWiley, 2010, xiii + 496 pages, € 100.00 / £ 80.50 / US$ 120.00, hardcoverISBN: 978-0-470-45463-3

Table of contents

1. Introduction to generalized linear models A.1. Background on basic test statistics2. Linear regression models A.2. Background from the theory of linear models3. Nonlinear regression models A.3. The Gauss–Markov theorem, var(ε) = σ 2i4. Logistic and Poisson regression models A.4. The relationship between maximum likelihood

estimation of the logistic regression model andweighted least squares

5. The generalized linear model6. Generalized estimating equation7. Random effects in generalized linear models A.5. Computational details for GLMs for a canonical

link8. Designed experiments and the generalizedlinear model A.6. Computations details for GLMs for a

noncanonical link

Readership: Students of statistics (undergraduate and postgraduate), users of statistics, practi-tioners in industry.The book has a Statistics-in-Industry flavour rather than, say, biostatistics or environmentalstatistics. The emphasis is on the practical application of GLMs (generalized linear models)rather than proofs of results, and is well aimed at a wide audience. Nevertheless, algebraicformulae for the various models and methods are given in detail. Due attention is given tomodel checking and diagnostics throughout, and the use of various computer packages (R, SAS,Minitab) is illustrated on many examples.

A thorough survey of linear and non-linear regression is given in Chapters 2 and 3, andthis is followed by detailed treatment of logistic and Poisson regression in Chapter 4 as aprelude to Chapter 5 (exponential family models, link functions, and GLMs in general). Thecoverage then goes beyond straightforward GLMs: the following three chapters are devotedto GEEs (generalized estimating equations), GLMMs (generalized linear mixed models), andexperimental design (with D-optimality).

Chapter 7, in particular, branches out in the last section to cover the Bayesian approach,showing how to implement McMC using WinBugs. This is a welcome inclusion of modern



statistical methodology. On the other hand, the chapter begins with a slightly odd description ofrandom effects; “patients in a biomedical study are often random effects” and “random effects arealmost always categorical.” However, the subsequent development correctly identifies randomeffects as individual-specific regression parameters, which are very often treated as beinggenerated by a normal (continuous, not categorical) distribution.

A particular strength of the book is the inclusion of many real data sets and these are madeavailable to be downloaded from a website. Many of these data sets are given to be analysed asexercises at the ends of chapters, though the “correct” results or solutions are not given.

Overall, I believe that this will find a place as a useful introduction to GLMs for those whowish to use them in practice—it is a good practical guide for engineers, scientists, and workingstatisticians.


London SW7 2AZ, UK



The Oxford Handbook of Applied Bayesian AnalysisAnthony O’Hagan, Mike West (Editors)Oxford University Press, 2010, xxxiv + 889 pages, £ 85.00 / US$ 150.00, hardcoverISBN: 978-0-19-954890-3

Table of contents

Part I: Biomedical and Health Sciences Part III: Environment and Ecology1. Flexible Bayes regression of epidemiologic

data (David B. Dunson)16. Assessing the probability of rare climate events

(Peter Challenor, Doug McNeall, JamesGattiker)2. Bayesian modelling for matching and

alignment of biomolecules (Peter J. Green,Kanti V. Mardia, Vysaul B. Nyirongo, YannRuffieux)

17. Models for demography of plant populations(James S. Clark, Dave Bell, Michael Dietze,Michelle Hersh, Ines Ibanez, Shannon L.LaDeau, Sean McMahon, Jessica Metcalf,Emily Moran, Luke Pangle, Mike Wolosin)

3. Bayesian approaches to aspects of the Vioxxtrials: non-ignorable dropout and sequentialmeta-analysis (Jerry Cheng, David Madigan) 18. Combining monitoring data and computer

model output in assessing environmentalexposure (Alan E. Gelfand, Sujit K. Sahu)

4. Sensitivity Analysis in microbial riskassessment: vero-cytotoxigenic E. coli O157 infarm-pasteurised milk (Jeremy E. Oakley,Helen E. Clough)

19. Indirect elicitation from ecological experts:from methods and software to habitatmodelling and rock-wallabies (Samantha LowChoy, Justine Murray, Allan James, KerrieMengersen)

5. Mapping malaria in the Amazon rain forest: aspatio-temporal mixture model (Alexandra M.Schmidt, Jennifer A. Hoeting, Joao Batista M.Pereira, Pedro P. Vieira) 20. Characterizing the uncertainty of climate

change projections using hierarchical models(Claudia Tebaldi, Richard L. Smith)

6. Trans-Study projection of genomic biomarkersin analysis of oncogene deregulation and breastcancer (Dan Merl, Joseph E. Lucas, Joseph R.Nevins, Haige Shenz, Mike West)

Part IV: Policy, Political and Social Sciences21. Volatility in prediction markets: a measure of

information flow in political campaigns(Carlos M. Carvalho, Jill Rickershauser)

7. Linking systems biology models to data: astochastic kinetic model of p53 oscillations(Daniel A. Henderson, Richard J. Boys, CaroleJ. Proctor, Darren J. Wilkinson)

22. Bayesian analysis in item response theoryapplied to a large-scale educational assessment(Dani Gamerman, Tufi M. Soares, Flavio B.Goncalves)

8. Paternity testing allowing for uncertainmutation rates (A. Philip Dawid, Julia Mortera,Paola Vicard) 23. Sequential multi-location auditing and the New

York food stamps program (Karl W. Heiner,Marc C. Kennedy, Anthony O’Hagan)

Part II: Industry, Economics and Finance9. Bayesian analysis and decisions in nuclear

power plant maintenance (Elmira Popova,David Morton, Paul Damien, Tim Hanson)

24. Bayesian causal inference: approaches toestimating the effect of treating hospital typeon cancer survival in Sweden using principalstratification (Donald B. Rubin, Xiaoqin Wang,Li Yin, Elizabeth R. Zell)

10. Bayes Linear uncertainty analysis for oilreservoirs based on multiscale computerexperiments (Jonathan A. Cumming, MichaelGoldstein) Part V: Natural and Engineering Sciences

11. Bayesian modelling of train doors reliability(Antonio Pievatolo, Fabrizio Ruggeri)

25. Bayesian statistical methods for audio andmusic processing (A. Taylan Cemgil, Simon J.Godsill, Paul Peeling, Nick Whiteley)12. Analysis of economic data with multiscale

spatio-temporal models (Marco A. R. Ferreira,Adelmo I. Bertoldey, Scott H. Holan)

26. Combining simulations and physicalobservations to estimate cosmologicalparameters (Dave Higdon, Katrin Heitmann,Charles Nakhleh, Salman Habib)

13. Extracting S&P500 and NASDAQ volatility:the credit crisis of 2007–2008 (Hedibert F.Lopes, Nicholas P. Polson) 27. Probabilistic grammars and hierarchical

Dirichlet processes (Percy Liang, Michael I.Jordan, Dan Klein)

14. Futures markets, Bayesian forecasting, and riskmodeling (Jose M. Quintana, Carlos M.Carvalho, James Scott, Thomas Costigliola) 28. Designing and analyzing a circuit device

experiment using treed Gaussian processes(Herbert K. H. Lee, Matthew Taddy, Robert B.Gramacy, Genetha A. Gray)

15. The new macroeconometrics: a Bayesianapproach (Jesus Fernandez-Villaverde, PabloGuerron-Quintana, Juan F. Rubio-Ramırez)

29. Multi-state models for mental fatigue (RaquelPrado)International Statistical Review (2010), 78, 3, 445–482

C© 2010 The Authors. International Statistical Review C© 2010 International Statistical Institute


Readership: Statisticians and quantitative researchers requiring a broad overview of theapplications of Bayesian statistics, or those interested in solutions to particular problems. Areference source for teachers and students in a wide variety of disciplines.

This weighty book provides a series of detailed and stimulating examples illustrating the powerfulelegance of the Bayesian approach to data analysis.

However, the title of the book may be misleading to potential purchasers. It should be “TheOxford Casebook . . . .” (Indeed, the editors describe the book as “a showcase of contemporaryBayesian analysis.”) I opened the book expecting to find descriptions focusing on Bayesianmethodology (from my Oxford dictionary: “a handbook: a book giving information such asfacts on a particular subject or instructions for operating a machine”) but instead found theaforementioned series of examples (from my Oxford dictionary: “a casebook: a written recordof cases dealt with”). I note that the back flap of the dustjacket describes the series as offering a“state-of-the-art survey of current thinking and research in a particular subject area,” examining“progress in the subject and [suggesting] directions for future research.” That seems rather atodds with the content of this volume, which shows the subject area in action, rather than thinkingand research about that subject area.

In this vein, I was a little taken aback to read, on the front flap, that the book was aimedat “statisticians and quantitative researchers requiring a broad overview of the applications ofBayesian statistics.” Are not Bayesian methods applied in all areas?

Fortunately, things are at least partially remedied by each chapter being accompanied by anappendix setting the methods used in that chapter into a broader Bayesian context, so going someway towards the stated series aim. I suspect that these appendices will be where the particularvalue of the volume lies.

Having said all that, if one approaches the book with expectations that its content will reflecta casebook rather than a handbook, it provides a marvellous tour of the Bayesian approach toproblems from a wide range of disciplines. It has been beautifully produced, and will certainlybe an attractive volume to dip into. Perhaps a nice present for a graduate student new to Bayesianideas, to open their eyes to the vast range of ways in which they are used.


London SW7 2AZ, UK

Estimation and tests in distribution mixtures and change-points modelsOdile PonsOdile Pons (French edition Hermes Science Publications), 2009, 200 pages, € 65.00, softcoverISBN: 978-2-9534122-0-8

Table of contents

1. Estimation and tests for finite parametricmixtures

6. Classification7. Continuous mixture models

2. Applications and particular cases 8. Mixtures and change-points for parametricregressions3. Partially observed parametric mixtures

4. Estimation and tests for nonparametric mixtures 9. Mixtures and change-points for auto-regressiveseries5. Nonparametric high dimensional mixtures

10. Nonparametric mixtures and change-points



Readership: Researchers, teachers and students in mathematical statistics and those interestedin the application of statistical methods.

This is an English version of the French Estimation et Tests dans les Modeles de Melanges et deRuptures, also published in 2009. The translation is a little idiosyncratic in places, but this doesnot detract from the sense of the book.

Mixture distributions have attracted considerable interest over the past 30 years, partly as aconsequence of their role in Bayesian analysis and partly as a consequence of their intrinsic meritas models of many real situations. The area has moved on substantially since the publication ofEveritt and Hand Finite Mixture Distributions in 1981, and is still an area of active work—drivenin significant part by the opportunities provided by increasingly powerful computers. This bookhas overlapping content with the more extensive Sylvia Fruhwirth-Schnatter’s Finite Mixtureand Markov Switching Models, published in 2006 (see International Statistical Review, Vol. 75,No. 2, p. 255), though neither author appears to cite any work by the other author. Some of thematerial appears here (or, more accurately, presumably in the original French edition) for thefirst time.

The book has a mathematical flavour, presented in classical theorem/proof style, and providinga rigorous description of the construction of estimators and test statistics, along with theirasymptotic behaviour. There are no examples of practical applications in the book. (The meaningof the word “application” in the title of Chapter 2 (“Applications and particular cases”) isindicated by the opening sentence of that chapter: “A direct application of the previous tests isthe selection of a model between two distinct regular models . . . .”) This means that the bookwill be of more value to those interested in the mathematical niceties of this important class ofstatistical models than those interested in practical data analysis.

The index is so short as to be of little value—as is demonstrated by the fact that there are noindex entries for words beginning with B, D, F, G, H, J, K, N,O, P, Q, S, U, V, W, X, Y, or Z.


London SW7 2AZ, UK

Introducing Monte Carlo Methods with RChristian P. Robert, George CasellaSpringer, 2010, xix + 283 pages, € 54.95 / £ 49.99 / US$ 64.99, softcoverISBN: 978-1-4419-1575-7

Table of contents

1. Basic R programming 5. Monte Carlo optimization2. Random variable generation 6. Metropolis–Hastings algorithms3. Monte Carlo integration 7. Gibbs samplers4. Controlling and accelerating convergence 8. Monitoring and adaptation for MCMC algorithms

Readership: Graduate students, researchers, and others with an interest in Monte Carlo methods,particularly MCMC methods.

This book may be divided into three parts. It begins with two introductory chapters, on Rprogramming and random number generation. Then come three chapters on Monte Carlomethods: on integration, convergence of methods, and optimization. The final three chapters



focus on MCMC methods: the Metropolis–Hastings algorithm, Gibbs sampling, and monitoringand adaptation for MCMC algorithms.

I found some aspects of the book very disappointing. The first chapter (“Basic R Program-ming”) has some unfortunate mistakes and some statements, which are contentious at least (“Anolder assignment operator is <- . . . but it should be ignored for cleaner programming”). Thegraphs throughout the book appear to be bitmaps—they are quite blurred. Either postscript or pdfgraphs should have been produced which would have immeasurably improved the presentation.

The book is not an easy read, requiring considerable background to be understood. I foundthe chapters on MCMC easier than the chapters on general Monte Carlo methods because I hada reasonable understanding of the theory beforehand. There are exercises within and at the endof all chapters, which appear to be fairly challenging, with the possible exception of the firsttwo chapters.

Overall, the level of the book makes it suitable for graduate students and researchers. Otherswho wish to implement Monte Carlo methods, particularly MCMC methods for Bayesiananalysis will also find it useful.

David Scott: [email protected] of Statistics, The University of Auckland

Private Bag 92019, Auckland 1142, New Zealand

Design of Observational StudiesPaul R. RosenbaumSpringer, 2010, xviii + 385 pages, € 79.95 / £ 72.00 / US$ 89.95, hardcoverISBN: 978-1-4419-1212-1

Table of contents

Part I Beginnings 11. Matching without groups1. Dilemmas and craftsmanship 12. Risk-set matching2. Causal inference in randomized experiments 13. Matching in R3. Two simple models for observational studies Part III Design Sensitivity4. Competing theories structure design 14. The power of a sensitivity analysis and its limit5. Opportunities, devices, and instruments 15. Heterogeneity and causality6. Transparency 16. Uncommon but dramatic responses to treatment

Part II Matching 17. Anticipated patterns of response7. A matched observational study Part IV Planning Analysis8. Basic tools of multivariate matching 18. After matching, before analysis9. Various practical issues in matching 19. Planning the analysis

10. Fine balance Summary: Key elements of design

Readership: Graduate students and researchers in statistics, biostatistics, econometrics, oracademic researchers in statistically oriented fields of psychology and social sciences.

The book has four parts. Part I is a conceptual introduction to causal inference in observationalstudies. Chapters 2, 3, and 5 of Part I cover concisely many of the ideas discussed in the book“Observational Studies (2002)” [Short Book Reviews, Vol. 22, p. 24] of the same author. Part IIconcerns the conceptual, practical, and computational aspects of creating a matched comparisonthat balances many observed covariates. In Part III the ability of competing designs to separatetreatment effects from unmeasured biases is discussed in detail for the first time in book form.Part IV discusses the activities that follow matching but precede analysis.



The author describes the flavour of his two related books as follows: “ObservationalStudies” writes about statistics, where “Design of Observational Studies” talks about statistics.Observational studies are common in most fields that study the effects of treatments on people.Strength of evidence provided by an observational study is determined largely by its design.The book is both an introduction to statistical inference in observational studies and a detaileddiscussion of the principles. The basic tools of multivariate matching are introduced withmany examples and with reference to implementation in R. The book will be suitable for aseminar course for graduate students with previous knowledge of the subject area, or practicingstatisticians seeking guidance in design of observational research and a language to discuss theissues. “Design of Observational Studies” is an important book.

Erkki P. Liski: [email protected] of Mathematics and StatisticsFI-33014 University of Tampere, Finland

Mathematics in Historical ContextJeff SuzukiMathematical Association of America, 2009, x + 409 pages, US$ 58.95, hardcoverISBN: 978-0-88385-570-6

Table of contents

1. Introduction 7. Early modern Europe2. The classical world 8. The eighteenth century3. China and India 9. The nineteenth century4. The Islamic world 10. The United States5. The Middle Ages 11. The modern world6. Renaissance and Reformation

Readership: Mathematicians with an interest in history, historians with an interest in mathemat-ics, or laymen with an interest in history and mathematics.

The title of this book, Mathematics in Historical Context, really catches the essence of whatthis book is all about: The development of mathematics, the lives of the most importantmathematicians at each time in history, and the historical context in which mathematics hasdeveloped and mathematicians have lived, from the ancient world of Egypt and Mesopotamiauntil the end of World War II. As such, the book is part history of mathematics, part mathematicalbiography, and part ordinary history. It describes how each civilization or society has developedand how mathematics has been used in it, the role mathematicians has played in world eventsand how both political events have affected the lives of mathematicians and how mathematicianshave affected political events.

It should be noted that statistics has an insignificant role in this text. A few importantstatisticians, such as A. N. Kolmogorov, Jerzy Neyman, and Abraham Wald are mentioned,but not, for example, Karl Pearson or R. A. Fisher. Important developments in probability arehowever discussed to some length. The book is suitable both for a laymen with an interest inhistory and mathematics and for a mathematician with an interest in history.





Bayesian Missing Data Problems: EM, Data Augmentation and NoniterativeComputationMing T. Tan, Guo-Liang Tian, Kai Wang NgChapman & Hall/CRC, 2010, xviii + 328 pages, £ 57.99 / US$ 89.95, hardcoverISBN: 978-1-4200-7749-0

Table of contents

1. Introduction 5. Computing posteriors in the EM-type structures2. Optimization, Monte Carlo simulation and

numerical integration6. Constrained parameter problems7. Checking compatibility and uniqueness

3. Exact solutions Appendix: Basic statistical distributions andstochastic processes4. Discrete missing data problems

Readership: Statisticians and students interested in statistical computation and Bayesianmethods.

There are two main aspects to the missing data problem: computational and inferential. Whilemuch recent work has focused on the latter, this book is firmly in the former camp, and sitsnicely alongside Tanner’s “Tools for Statistical Inference.” “Missing Data” then is used here inthe computational sense, that is to refer to problems which can be formulated in terms of missingdata, including augmented data problems, those with latent variables and so on. For Bayesiansmany such problems can be brought into the fold of MCMC, and in this sense are “solved.” Theauthors of this book are less sanguine about MCMC, and their stated aim is, where possible, toavoid the use of Markov chain based simulation for such problems, and instead use exact resultsor non-iterative sampling methods. For them, the key to this is their so-called “Inverse BayesFormula (IBF),” which expresses, in the auxiliary variable setting, the prior distribution terms ofthe posterior and the likelihood. In fact they have two forms of this equivalence. While obviouslynot the goal of a data analysis, they show how these relationships have valuable computationalimplications.

The book opens with an introduction to Bayes methods in the missing data setting and totheir IBF expressions. The second chapter is a very nice summary of well-used computationaltechniques in statistics and is of wide applicability. The next four chapters develop the authors’ideas in a very wide variety of settings, starting with simpler problems with exact solutions, andmoving to more complex ones with some quite intricate simulation schemes. The final chapter isof less direct interest, dealing as it does with mathematical technicalities concerning conditionaldistributions and their related joint distributions.

For those interested in Bayesian computational methods this book will be of great interest.However, I am not yet convinced that the methods will replace our current reliance on MCMCmethods because, in spite of the great ingenuity in the authors’ derivations, the principal ideasseem to need expression through algebraic manipulation, and the book gives the impression ofa long series of one-off solutions, in direct contrast to the remarkable computation flexibilityand generality of MCMC methods as implemented in WinBUGS. Hence, based on what I haveread here, I could not envisage an IBF-based analogue of WinBUGS, but perhaps that is the nextstep.

Mike Kenward: [email protected] School of Hygiene & Tropical Medicine

Keppel Street, London WC1E 7HT, UK



Mixed Effects Models for Complex DataLang WuChapman & Hall/CRC, 2010, xx + 419 pages, £ 57.99 / US$ 89.95, hardcoverISBN: 978-1-4200-7402-4

Table of contents

1. Introduction 7. Survival mixed effects (frailty) models2. Mixed effects models 8. Joint modeling longitudinal and survival data3. Missing data, measurement errors, and outliers 9. Robust mixed effects models4. Mixed effects models with missing data 10. Generalized estimating equations (GEEs)5. Mixed effects models with covariate

measurement errors11. Bayesian mixed effects modelsAppendix: Background materials

6. Mixed effects models with censoring

Readership: Researchers in statistics, users of (fairly sophisticated) statistics.

The type of data considered is typically longitudinal, with repeated measurements of responseand covariates made on each individual unit over time. There may be missing values ofcovariates, time-dependent or not, and response, with missing mechanisms of various kinds:missing completely at random (MCAR), missing at random (MAR) and missing not at random(MNAR). In addition, measurement error, censoring, dropouts, intermittent observation, andoutliers are considered. So, the “Complex Data” in the title is well justified.

The models applied to such data include linear and non-linear normal models, and generalizedlinear models, usually incorporating both fixed and random effects (“Mixed Effects Models”).Beside such parametric models, non-parametric and semi-parametric versions are also covered.As well as modelling responses in terms of covariates, models for missing data mechanisms areincluded. Some of the models proposed look a little elaborate against which it can be arguedthat one has to do something, particularly when several of the “complexities” occur together.Also, the author does emphasize assessing model fit and identifiability issues.

The statistical method is mainly maximum likelihood estimation and likelihood ratio testing(with AIC, etc.). Robust methods, GEE, and the Bayesian “method” are each given a chapter atthe end. Various strategies are described for handling missing data: these include a variety ofimputation methods and direct integration.

At the core of the numerical methods are integration and maximization. Integration is used tointegrate out random effects, missing values, etc. Gauss–Hermite quadrature, Monte Carlosimulation, Taylor expansion and Laplace approximation are all covered but not adaptivetrapezium rules. For maximization of the likelihood the EM algorithm is the main choice.This is so even when EM is awkward to implement in either or both E and M steps. In suchcases it might be better to apply a modern quasi-Newton algorithm, such as BFGS, directly tothe likelihood.

There are, inevitably, a few minor gripes. Some of the basic material, such as the discussionof numerical methods, is repeated when new models are introduced. This seems unnecessarybut is maybe no bad thing for teaching students; it also makes it easier for experienced usersto dip in to a particular section for reference. However, as an introduction to what it says in thetitle of the book, the author has done an excellent job—the coverage is pretty comprehensive,detailed without too much mathematical technicality, and (most importantly) readable. I believethat it will become a useful reference in many libraries, personal and public.


London SW7 2AZ, UK



New Frontiers in Microsimulation ModellingAsghar Zaidi, Ann Harding, Paul Williamson (Editors)Ashgate, 2009, 635 pages, £ 37.50 / US$ 74.95, softcoverISBN: 978-0-7546-7647-8

Table of contents

Preface—Orcutt’s vision 50 years on (M. Wolfson) 11. Projecting pensions and age at retirement inFrance: some lessons from the Destinie I model(D. Blanchet, S. Le Minez)

1. New Frontiers in Microsimulation Modelling:Introduction (P. Williamson, A. Zaidi, A.Harding) 12. Rates of return in the Canada pension plan:

sub-populations of special policy interest andpreliminary after-tax results (R. J. Morrison)

Part I Spatial Modelling2. Moses: dynamic spatial microsimulation with

demographic interactions (M. Birkin, B. Wu, P.Rees)

13. Simulating employment careers in the lifepathsmodel: validation across multiple time scales(G. T. Rowe, K. D. Moore)3. Small area poverty estimates for Australia’s

Eastern seaboard in 2006 (R. Tanton, J.Mcnamara, A. Harding, T. Morrison)

14. Employment transitions and earnings dynamicsin the SAGE model (A. Zaidi, M. Evandrou, J.Falkingham, P. Johnson, A. Scott)4. Microsimulation as a tool in spatial decision

making: simulation of retail developments in aDutch town (E. S. van Leeuwen, G. P. Clarke,P. Rietveld)

15. Simulating earnings in dynamicmicrosimulation models (C. O’Donoghue,R. H. Leach, S. Hynes)

5. Time and money in space: estimatinghousehold expenditure and time use at thesmall area level in Great Britain (B. Anderson,P. de Agostini, S. Laidoudi, A. Weston, P. Zong)

16. Continuous-time microsimulation inlongitudinal analysis (F. Willekens)

17. Welfare effects of alternative financing ofsocial security: calculations for Belgium (B.Capeau, A. Decoster, K. de Swerdt, K. Orsini)Part II Work Incentives and Labour Supply

6. Work incentives, redistribution policies and theequity-efficiency trade off: evidence fromSpain (J. M. Labeaga, X. Oliver, A. Spadaro)

18. Shooting at moving targets: short- versuslong-term effects of anti-poverty policies(R. R. G. de Blander, I. Nicaise)

7. Microsimulating supply/demand interactionson a labour market: a prototype (M. Barlet, D.Blanchet, T. Le Barbranchon)

Part IV: Macro-Micro Linkages and EnvironmentalPolicies

19. A dynamic analysis of permanently extendingthe 2001 and 2003 tax cuts: an application oflinked macroeconomic and microsimulationmodels (T. L. Foertsch, R. A. Rector)

8. Policy swapping across countries usingEUROMOD: the case of in-work benefits inSouthern Europe (F. Figari)

9. Behavioural microsimulation: labour supplyand child care use responses in Australia andNorway (G. Kalb, T. O. Thoresen)

20. Linking microsimulation and macro-economicmodels to estimate the economic impact ofchronic disease prevention (L. J. Brown, A.Harris, M. Picton, L. Thurecht, M. Yap, A.Harding, P. B. Dixon, J. Richardson)

Part III Demographic Issues, Social Security andRetirement Income

10. Fertility decisions—simulation in anagent-based model (IFSIM) (E. Baroni, M.Eklof, D. Hallberg, T. Lindh, J. Zamac)

21. Microsimulation meets general equilibrium—anew tool for applied policy analysis (M.Clauss, S. Schubert)

22. Higher immigration—empirical analyses ofdemographic and economic effects for Norway(N. M. Stølen, I. Texmon, V. O. Nielsen)

23. Complying with the Kyoto targets: anassessment of the effectiveness of energy taxesin Italy (R. Bardazzi, F. Oropallo,M. G. Pazienza)

Readership: Researchers in microsimulation, as well as other fields of socio-economicmodelling. The broad scope of methodologies and applications makes the book also interestingto others.



Microsimulation models in socio-economic research discern from other models in that theymodel on the level of the individual and his or her household. The book discussed here drawstogether some of the papers presented during the first General Conference of the newbornInternational Microsimulation Association in 2007.

The conference marked the 50th year anniversary of the 1957 Review of Economics andStatistics paper in which Guy Orcutt presented his views on what would later be known asmicrosimulation. The book celebrates this anniversary by means of a rich chapter by MichaelWolfson. This chapter should be a mandatory read for all those involved in microsimulation.

The first part of the books covers four papers on spatial microsimulation modelling. Spatialmodels obviously have the spatial location of an individual and his or her household the coreendogenous variable. These models among other things can be used to simulate within-city movesand housing stock dynamics, differences in poverty rates between rural and urban regions.

The second part of the book includes four chapters that discuss the impact of demographicageing and a variety of policy measures on work incentives and labour supply. The papers in thispart of the book aim to bring micro-simulation models beyond the first-round effects of policychange, that is, changes induced by otherwise unchanged estimated discrete choice models.

The third part of the book deals with demography, social security and retirement incomes.This part includes no less than nine papers, reflecting the fact that especially dynamic micro-simulation models traditionally have been focussing on demographics and the re-distributiveimpact of retirement systems.

The title of the fourth and final part is “Macro-micro linkages and environmental policies.”But the latter part of the title pertains to one chapter only. Micro-simulation models allow for theassessment of re-distributive impacts but often do not take into account macroeconomic impactsof reform. This fourth part consists of four chapters offsetting this problem.

The wide menu of subjects and methodologies that it covers makes this an impressive book.It serves both specialists as well as the interested layman. The first can use this book to lookover the hedge; the second gets the Wolfson paper and the introductory chapter as a first dish,and can then check the chapters’ list for main course. But the broad orientation comes at aprice, which is that one rapidly gets lost between all these models and applications. The bookwould have benefited from a more focussed introductory chapter, outlining a more traditionalclassification of microsimulation models, extended by spatial models, for a layman audience.Then the individual chapters could have been set in a more general framework, taking advantageof the pros, while working around the cons. This, however, is a minor comment on an otherwisevery interesting and accessible book.

Gijs Dekkers: [email protected] Planning Bureau, Avenue des Arts 47-49, 1000 Brussels, Belgium

and Centre for Sociological Research, Katholieke Universiteit LeuvenParkstraat 45 - bus 3601, 3000 Leuven, Belgium


nonparametric statistics for non-statisticians: a step-by-step approach by gregory w. corder, dale...

Documents