contents
TRANSCRIPT
Computational Statistics and Data Analysis 66 (2013) vi–vii
Volume 66, October 2013
CONTENTS
Section I: Computational Statistics
R.J. Evans and A. ForcinaTwo algorithms for fitting constrained marginal models 1
S. GaureOLS with multiple high dimensional category variables 8
Section II: Statistical Methodology for Data Analysis
A.W. Bowman and R.M. CrujeirasInference for variograms 19
S.-H. Hsieh, C.-S. Li and S.-M. LeeLogistic regression with outcome and covariates missing separately or simultaneously 32
F. BachocCross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussianprocesses with model misspecification 55
M. Amini and N. BalakrishnanNonparametric meta-analysis of independent samples of records 70
Y. ZuoMultidimensional medians and uniqueness 82
S. Lee, H. Shin and N. BillorM-type smoothing spline estimators for principal functions 89
H. Zhao, P.-Y. Zhao and N.-S. TangEmpirical likelihood inference for mean functionals with nonignorably missing response data 101
J. Morio, D. Jacquemart, M. Balesdent and J. MarzatOptimisation of interacting particle systems for rare event estimation 117
H. Lin, Y. Li and M.T. TanEstimating a unitary effect summary based on combined survival and quantitative outcomes 129
C. Wang, L. Cao and B. MiaoOptimal feature selection for sparse linear discriminant analysis and its applications in geneexpression data 140
(Continued)
http://dx.doi.org/10.1016/S0167-9473(13)00193-X
Computational Statistics and Data Analysis 66 (2013) vi–vii vii
X. Wang, D. Lu and L. SongStatistical inference for partially linear stochastic models with heteroscedastic errors 150
I. Ntzoufras and C. TarantolaConjugate and conditional conjugate Bayesian analysis of discrete graphical models ofmarginal independence 161
D. Yan, A. Chen and M.I. JordanCluster Forests 178
Section III: Special ApplicationsP. WittekTwo-way incremental seriation in the temporal domain with three-dimensional visualization:Making sense of evolving high-dimensional datasets 193
K.A. Tamura and V. GiampaoliNew prediction method for the mixed logistic model applied in a marketing problem 202
http://dx.doi.org/10.1016/S0167-9473(13)00193-X