This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 29
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #309023
Title: Additive Models with Spatio-Temporal Data
Author(s): Xiangming Fang*+ and Kung-Sik Chan
Companies: East Carolina University and The University of Iowa
Address: Department of Biostatistics, Greenville, NC, 27858,
Keywords: Additive models ; Penalized likelihood ; Matérn class ; Spatio-temporal data ; ML ; REML

The current methods for fitting additive models with correlated data are not completely satisfactory. We propose a new approach to fit additive models with spatio-temporal data via the penalized likelihood approach which estimates the smooth functions and covariance parameters by iteratively maximizing the penalized log likelihood. Both ML and REML estimation schemes are developed. The asymptotic distribution of the estimates is studied in a Bayesian framework. Conditions for asymptotic posterior normality are investigated for the case of separable spatio-temporal data with fixed spatial covariance structure and no temporal dependence. We also propose a method to check the assumption of temporal independence and a new model selection criterion for comparing models with and without spatial correlation. The proposed methods are illustrated by both simulation study and real data analysis.

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