Spatial modeling with spatially varying coefficient processes
成果类型：
Article
署名作者：
Gelfand, AE; Kim, HJ; Sirmans, CF; Banerjee, S
署名单位：
Duke University; University of Oulu; University of Connecticut; University of Minnesota System; University of Minnesota Twin Cities
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1198/016214503000170
发表日期：
2003
页码：
387-396
关键词：
摘要：
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.