Modeling Spatio-Temporal Data with Additive Models

• Activity Number: 41
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Xiangming Fang*+
• Companies: East Carolina University
• Keywords: Additive models ; penalized likelihood ; restricted maximum likelihood ; spatio-temporal data ; Matern model

Abstract

Additive models have been widely used. While the procedure for fitting an additive model to independent data has been well established, not as much work has been done when the data are correlated. The currently available methods are not completely satisfactory in practice. A new approach is proposed 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 maximum likelihood (ML) and restricted maximum likelihood (REML) estimation schemes are developed. Also, conditions for asymptotic posterior normality are investigated for the case of separable spatio-temporal data with fixed spatial covariate structure and no temporal dependence. We propose 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 on the abundance distribution of Alaska plaice in eastern Bering Sea.