Spatial modeling with spatially varying coefficient processes

doi:10.1198/016214503000170

Spatial modeling with spatially varying coefficient processes

2003-06-01

509

PRE

AI

DOI：10.1198/016214503000170

Original

Original request for help

Save

Abstract

En 中文

In many applications, the objective is to build regression models to explain a response variable over a region of interest under the assumption that the responses are spatially correlated. In nearly all of this work, the regression coefficients are assumed to be constant over the region. However, in some applications, coefficients are expected to vary at the local or subregional level. Here we focus on the local case. Although parametric modeling of the spatial surface for the coefficient is possible, here we argue that it is more natural and flexible to view the surface as a realization from a spatial process. We show how such modeling can be formalized in the context of Gaussian responses providing attractive interpretation in terms of both random effects and explaining residuals. We also offer extensions to generalized linear models and to spatio-temporal setting. We illustrate both static and dynamic modeling with a dataset that attempts to explain (log) selling price of single-family houses.

Keywords:

Bayesian framework

multivariate spatial processes

prediction

spatio-temporal modeling

stationary Gaussian process

Spatial modeling with spatially varying coefficient processes

Abstract

AI Summary

Journal

Organization