a latent approach to the statistical analysis of space-time data dani gamerman
DESCRIPTION
A Latent Approach to the Statistical Analysis of Space-time Data Dani Gamerman Instituto de Matemática Universidade Federal do Rio de Janeiro Brasil http://acd.ufrj.br/~dani 17th International Workshop on Statistical Modelling Chania, Crete, Greece, 8-12 July 2002. World Cup Algorithm. - PowerPoint PPT PresentationTRANSCRIPT
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A Latent Approach to the Statistical Analysis of Space-time Data
Dani GamermanInstituto de Matemática
Universidade Federal do Rio de JaneiroBrasil
http://acd.ufrj.br/~dani
17th International Workshop on Statistical ModellingChania, Crete, Greece, 8-12 July 2002
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1990
1974
Played in Europe
World Cup Algorithm
?2006
1958
Played in Europe
2002
1962
Played in exotic place
1970
1994
Played in (L) America
1986
1978
Played in L. America
1966
1998
One-time only home win Europe 25 miles apart
Center of theworld football
1982
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A Latent Approach to the Statistical Analysis of Space-time Data
Dani GamermanInstituto de Matemática
Universidade Federal do Rio de JaneiroBrasil
http://acd.ufrj.br/~dani
Joint work withMarina S. Paez (IM-UFRJ)Flavia Landim (IM-UFRJ)
Victor de Oliveira (Arkansas)Alan Gelfand (Connecticut)
Sudipto Banerjee (Minnesota)
17th International Workshop on Statistical ModellingChania, Crete, Greece, 8-12 July 2002
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Introduction
Examples: 1) measurements of pollutants in time over a set of monitoring stations
3) counts of morbidity/mortality events in time over a collection of geographic regions
Environmental science – data in the formof a collection of time series that are geographically referenced.
Some examples can be found in other areas
2) selling price of properties around a neighborhood of interest
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Main Objective: spatial interpolation
Example: Pollution in Rio de Janeiro
Paez, M.S. and Gamerman, D. (2001). Technical report. Statistical Laboratory, UFRJ.
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Example: Pollution in Rio de JaneiroProb ( PM10 > 100 g/m3 | Yobs )
Other features of interest can be obtained
Picture showed mean interpolated values
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Spatial Interpolation
m = number of observations
g = number of grid points
s1, ... ,sm = observed sites
s1n,...,sg
n = grid points (to interpolate)
Y1n,...,Yg
n = observations in the grid points
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dYpYYpYYp obsobsnobsn )|(),|()|(
- all model parametersYmis - missing data, treated as parameters
1. Frequentist inference: generate Yn from ),|( obsn YYp
• Obtain P(Yn|Yobs) by simulation.
Steps to generate from Yn|Yobs :
If with probability 1 then
),|()|( * obsnobsn YYpYYp
2. Bayesian inference: i ) generate from
ii ) generate Yn from
)|( obsYp
),|( obsn YYp
Interpolation )|( obsn YYp
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Usual simplifications:
where = ( 1, ... , n ) with i=E[w(si)] and
= (ij [w(si), w(sj)] )i,j
Gaussian Process (GP)(or Gaussian Random Field)
S - region of Rp (in general, p=2)
{ w(s) : s S } is a GP ifn, s1 , ... , sm S
( w(s1) , ... , w(sn) ) ~ Nn (, )
2) Homoscedasticity i = , i
Notation: w(.) ~ GP((.),,)
1) Isotropy [w(si),w(sj)]= (hij) with hij=|si– sj|
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Statistical Analysis
Starting point: regression models
Yt(s) = t(s) + e t(s) wheret(s) = 0 + 1 X t1(s) + ... + pXtp(s) andet(s) ~ N(0, e
2) independent
Suppose that Xtj(s) handles temporal autocorrelation Otherwise, we can include a temporal component t
Usually et(s) remains spatially correlated
In this case, et(s) = e0(s) + et1(s) e0(s) errors spatially correlated et1(s) pure residual (white noise) 0(s) = 0 + e0(s)
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Inference 1. At first (3 steps)
• How to estimate 0(s) ?
Traditional approach: geostatistical 0(.) ~ GP(0,,) ore0(.) = 0(.) 0 ~ GP(0,,)
(b) e2, 2 and 0 estimated from rt0(s)
0ˆ and ˆ,ˆ e(c) Inference based on
(a) 0 , 1 , ... , p estimated in the regression model and the residuals rt0(s) = Yt (s) t(s) are constructed
then, 0obs ~ N(0 1, , R)
0obs = (0(s1) , ... , 0(sm) )
Hiperparameters: e2, 2 and 0
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3) Natural solution (Kitanidis, 1986; Handcock & Stein, 1993): • specify distribution for 0 • perform Bayesian inference
Problems: (a) rt0(s) et (s)(b) )ˆ,ˆ,ˆ( 0e ),,( 0e
2) next step:• 0 , 1 , ... , p and estimated jointly solves (a)• but to incorporate uncertainty about is complicated
0,, e
0ˆ,ˆ,ˆ e
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Recall: E[Yt(s)]=0(s) + 1Xt1(s) + ... + pXtp(s)
Spatial heterogeneity doesn’t have to be restricted to 0
Model generalization
Example: site by site effect of temperature in the Rio pollution data
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Extension of the previous modelE [Yt(s)] = 0(s) + 1(s)Xt1(s) + ... + p(s)Xtp(s)
previous model E [Yt(s)] = 0(s) + 1 Xt1(s) + ... + p Xtp(s)
Hyperparameters: = (ewhere = (0, 1,..., p)Special cases for the j(.)´s:
One possibility: (.) ~ GP(, , )
a) prior independence
),...,( 220 pdiag
(.))(.),...,( 0 p
b) same spatial structure and prior correlation between the j(.)´s
jj (.),(.)
We can accommodate spatial variation for other coefficients j, j=1, ... , p.
(.) = (0(.), 1(.),..., p(.))
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How to estimate j(s), j=0,1,...,p ?
2) natural solutions:Specify prior distribution for In general, independent and non informative priors are used
Problems (the same as before): (a) bj(s) j(s)
(b) ),,,( e)ˆ,ˆ,ˆ,ˆ( e
1) classical solution (Oehlert, 1993; Solna & Switzer, 1996):
(a) 0 (s), 1 (s), ... , p (s) estimated by
b0(s), b1(s), ... , bp (s) in the local regression model
(b) estimated from the bj(s)
(c) inference based on ˆ,ˆ,ˆ,ˆ e
,,, e
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Model Summary
Parameters: obs , where = ( ,e2, ,)
jobs = (j(s1) , ... , j(sm) ), j=0, 1, ... , p
obs = (0obs , ... , p
obs )= ( 0 , 1 , ... , p )
Data: Yobs = (Y1(s1) , ... , YT(sm)) Xobs = (X1(s1) , ... , XT(sm))
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Simulated data
Yt(s) = t(s) t(s), t=1,...,30 t(s) = 0(s)+ 1(s) Xt(s)
t(s) ~ N(0, e2) independent with e
2=1
0 ~ N(, ,(1 ~ N(, ,( Xt(s) ~ N(, ,(, for all time t
Exponential correlation functions: j(x)exp{- j x}
0= 100 1= 5 2= 00= 0.4 1= 0.8 2= 1.5 0
2= 0.1 1
2= 1 2
2= 0.333
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+
=
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1X
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Y
Simulated Data
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Inference
Parameters: (obs ,)= ( ,e
2, ,)
Likelihood:L(obs ,) = p(Yobs | obs , e
2 )
Prior:p(obs ,)= p( obs | ) p() p(e
2) p() p()
Posterior:(obs ,) L (obs ,) p(obs ,)
• Many parameters
• Complicated functional form
• Solution by MCMC
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again, use jobs as data (geostatistical analysis)
(c) [ e2 | rest ] ~ [ e
2 | Yobs , obs ] ~ Inverse Gamma
Full Conditionals
(a) [ obs | rest ] ~ Normal
(b) [ | rest] ~ Normal
(e) [ | rest ] ~ j p(j | jobs , j, ) p()
use jobs as if they were data
hard to sample Metropolis - Hastings
(d) [ |rest ] ~ [ | obs , , Inverse Wishart
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Results(based on a regular grid of m=25 sites)
Histogram of the parameters
i = i
-2
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Spatial Interpolation
Interpolation grid: s1n , ... , sg
n
jn = (j(s1
n) , ... , j(sgn) ), j=0, 1, ... , p
n = (0n , ... , p
n )
We need to obtain the interpolation of j´s to interpolate Yn
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Interpolation of Y´s
(Yn,n,| Yobs) = (Yn|n, , Yobs) (n,| Yobs)
= (Yn| n ,) (n,| Yobs)
Simulation of [Yn |Yobs] also in 2 steps:
(a) [ n, | Yobs ] MCMC and Spatial Interpolation
(b) [ Yn| n ,] using Multivariate Normal
Spatial Interpolation of ´s (n,obs,| Yobs) = ( n | obs, , Yobs) ( obs, | Yobs) = ( n | obs ,) ( obs, | Yobs)
Simulation of [ n | Yobs ] in 2 steps:(a)[ obs, | Yobs ] using MCMC(b)[ n | obs ,] using Multivariate Normal
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Simulated data: Interpolation of 1
Simulated values
Interpolated values
0.1 0.3 0.5 0.7 0.9
Coordenada 1
0.1
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ord
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ad
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0.1 0.3 0.5 0.7 0.9
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0.1 0.3 0.5 0.7 0.9
Coordenada 1
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ord
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Simulated data: Interpolation of Y30(.)
Simulated values
Interpolated values
0.1 0.3 0.5 0.7 0.9Coordenada 1
0.1
0.3
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Co
ord
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ad
a 2
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Interpolation of X´s
These interpolations assume that the interpolated covariates Xj are available for j=1, ... , p
Otherwise, we must interpolate them
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Simulation of [Xn|Yobs,Xobs] in 2 steps:
(a) [x | Xobs ] MCMC
(b) [Xn| x, Xobs ] using Multivariate Normal
Model may be completed with
X(.) | x , x , x ~ GP(x, x , x(.))
(Xn, x | Yobs , Xobs) = (Xn , x| Xobs ) = (Xn| x, Xobs) (x | Xobs )
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Results obtained by interpolating X
Histogram of the parameters
Precisions less sparse then when X is known
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Interpolation of X30(.)
0.1 0.3 0.5 0.7 0.9
lat
0.1
0.3
0.5
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lon
g
0.1 0.3 0.5 0.7 0.9
Coordenada 1
0.1
0.3
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ord
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ad
a 2
Simulated values
Interpolated values
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Interpolation of Y30(.)
Known X
Unknown X
0.1 0.3 0.5 0.7 0.9
Coordenada 1
0.1
0.3
0.5
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ord
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0.1 0.3 0.5 0.7 0.9
Coordenada 1
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113.2
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Application to the pollution data
t(s) independents N(0,2)
0~ N(, ,(.1~ N(, ,(.i(., i=0,1 are exponential correlation functions
Yt (s) = square root of PM10 at site s and time t
Xt = (MON, TUE, WED, THU, FRI, SAT)
Yt(s) = 0 (s) + 1(s)TEMPt + ´ Xt t(s)
• Now, the temperature coefficient varies in space
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Results for the pollution data in Rio
Histogram of the hiperparameters sample
where i = i -2
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Interpolation of the coefficient
Prior for G(10,10c)
SSDE = 0.0637
G(10-3,10-3)
SSDE = 0.1444
m
i
olsi
obsi YsE
m 1
2}ˆ]|)([{1
where SSDE =
Same idea can be used for (explanatory geostatistical analysis)
c obtained by exploratory analysis site by site (OLS)
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Yt(s)= t(s) + et(s) where t(s)=t0(s)+t1(s)Xt1(s)+...+tp(s)Xtp(s) et(s) ~ N(0, e
2) independent
Extension of the previous model
Yt(s)= t(s) + et(s) where t(s)= 0(s)+ 1(s)Xt1(s)+...+ p(s)Xtp(s) et(s) ~ N(0, e
2) independent
previous model
Natural specification t(.) | t ~ GP(t , ,), independent in time
The model must be completed with:(a) prior for (e , , as before(b) specification of the temporal evolution of the t´s
We can also accommodate temporal variation of the coefficients j, j=0,...,p.
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Suggestion - use dynamic models (SVP/TVM)(Landim & Gamerman, 2000)
t | t-1 ~ N( Gt t-1 , Wt )
unknown parameters of the evolution
Model parameters: obs , , = ( 0 , e
2 , , , W )
where = ( 1, ... , T) andt = ( t0 , t1, ... , tp ), t=1, ... , T
Simulation cycle has 2 changes:I) additional step to II) modified step to
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Application to simulated data
Yt (s) = t0 (s) + t1(s)Xt1(s) + t(s)t(.) ~GP (t, ,)t = t-1 + t
same spatial correlation to 0 and 1
(. exponential correlation function with = 1.Histogram of the posterior of
Multivariate observations: Yt (s) = (Yt1 (s), Yt2 (s))
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Trajectory of (t) - mean and credibility limits
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Interpolation
Samples from ytn|yobs are obtained through the algorithm below:
1. Sample from tobs, yobs - through MCMC
2. Sample from tnt
obs- through Gaussian process
3. Sample from ytnt
n - Independent Normal draws
Once again, Xtn must be known, otherwise, they will have to be
interpolated.
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Spatially- and time-varying parameters (STVP)
Not separable at the latent level, unlike SVP/TVM
Another possibility: temporal evolution applied directly to the t processes rather than to their means
Yt (s) = t0 (s) + t1(s)Xt1(s) + t(s)t(.) = t-1(.) + wt(.) wt(.) ~ GP (t, ,) independent in time
SVP/TVM:|)(|)(]},min{[)](),(cov[ 21212121 21
ssttIttRss tt
Completed with: 0(.) = 0 ~ N(g0,R)
STVP:|)](|}[,min{)](),(cov[ 212121 21
ssWttRss tt
Marginal Prior:t(.)| t ~ GP (t, t ,)
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Computations
MCMC algorithm must explore the correlation structures parameters are visited in blocks
(Landim and Gamerman, 2000; Fruhwirth-Schnatter, 1994)
Based on the forecast distribution of YT+h|Yobs,for YT+h = (YT+h(s1
f ),..., YT+h(sFf )), and any collection (s1
f,..., sFf)
1. Sample from Tobs, Yobs - through MCMC
2. Sample from T+hfT
obs- obtained by introduction of T+hobs
3. Sample from YtnT
n - Independent normal draws
Tobs
T+hobs by successive evolution of the process
T+hobs T+h
f by interpolation with gaussian process
Prediction
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Time-varying locations
Assume locations st = (st1,..., st
nt) at time t t
obs is a nt-dimensional vector, t = 1,...,T
1111~
),|~
,(),|( tobstt
obst
obst
obst dpp
1111~
),|~
(),~
|( tobsttt
obst dpp
Both densities in the integrand are multivariate normal
The convolution of these two densities can be shown to be normal and required evolution equation for can be obtained
SVP/TVM: Easily adapted STVP: requires introduction of for updated locations1
~t
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Non-Gaussian Observations
Two distinct types of non-normality data:
• Count data:
• Continuous:
Can be normalized after suitable transformation g(y) Example: Rio pollution data ))(( YYg estimated jointly with other model parameters (de Oliveira, Kedem and Short, 1997)
For example, in the bernoulli or poisson form standard approach: yt(s) ~ EF(t(s)) spatio-temporal modeling issues: similar computations: harder
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Non-Gaussian Evolution
Abrupt changes in the process normality is not suitable
Robust alternative:wt(.) ~ GP(t,,) is replaced bywt(.)| t ~ GP(t,t
-1, ) and t ~ G(t, t), independent for t=1,...,T
Therefore, wt(.) ~ tP(t,,)
t’s control the magnitude of the evolution
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Final Comments
• More flexibility to accommodate variations in time and space.
• Static coefficient models: samples from the posterior were generated in the software BUGS, with interpolation made in FORTRAN
• Extension to accommodate anisotropic processes to some components of the model.
• Extensions to observations in the exponential family and estimation of the normalizing transformation.
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A Latent Approach to the Statistical Analysis of Space-time Data
Dani GamermanInstituto de Matemática
Universidade Federal do Rio de JaneiroBrasil
http://acd.ufrj.br/~dani
17th International Workshop on Statistical ModellingChania, Crete, Greece, 8-12 July 2002