Model comparison and selection for stationary space-time models

被引:24
作者
Huang, H-C.
Martinez, F.
Mateu, J.
Montes, F.
机构
[1] Univ Jaume 1, Dept Math, E-12071 Castellon de La Plana, Spain
[2] Univ Valencia, Dept Stat & OR, E-46100 Burjassot, Spain
[3] Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan
关键词
AIC; BIC; Kalman filter; maximum likelihood; nonseparable and separable spatio-temporal covariance function; surface shortwave radiation budget (SSRB);
D O I
10.1016/j.csda.2006.07.038
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
An intensive simulation study to compare the spatio-temporal prediction performances among various space-time models is presented. The models having separable spatio-temporal covariance functions and nonseparable ones, under various scenarios, are also considered. The computational performance among the various selected models are compared. The issue of how to select an appropriate space-time model by accounting for the tradeoff between goodness-of-fit and model complexity is addressed. Performances of the two commonly used model-selection criteria, Akaike information criterion and Bayesian information criterion are examined. Furthermore, a practical application based on the statistical analysis of surface shortwave radiation budget (SSRB) data is presented. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:4577 / 4596
页数:20
相关论文
共 53 条
[11]  
Christakos G, 1998, SPATIOTEMPORAL ENV H
[12]   Classes of nonseparable, spatio-temporal stationary covariance functions [J].
Cressie, N ;
Huang, HC .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1999, 94 (448) :1330-1340
[13]   Estimating and modeling space-time correlation structures [J].
De Cesare, L ;
Myers, DE ;
Posa, D .
STATISTICS & PROBABILITY LETTERS, 2001, 51 (01) :9-14
[14]  
De Cesare L, 2001, ENVIRONMETRICS, V12, P11, DOI 10.1002/1099-095X(200102)12:1<11::AID-ENV426>3.3.CO
[15]  
2-G
[16]  
De Iaco S, 2001, STAT PROBABIL LETT, V52, P21
[17]  
DIMITRAKOPOULOS R, 1994, QUANT GEO G, V6, P88
[18]  
FERNANDEZCASAL R, 2003, THESIS SANTIAGO COMP
[19]  
FUENTES M, 2003, 2533 N CAR STAT U
[20]   Nonseparable, stationary covariance functions for space-time data [J].
Gneiting, T .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2002, 97 (458) :590-600