期刊
SPATIAL STATISTICS
卷 1, 期 -, 页码 30-39出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.spasta.2012.02.005
关键词
Data fusion; Directional data; Dirichlet processes; Extreme values; Kernel predictors; Species distributions
类别
资金
- NSF [DMS 0914906, CDI 0940671]
This short paper is centered on hierarchical modeling for problems in spatial and spatio-temporal statistics. It draws its motivation from the interdisciplinary research work of the author in terms of applications in the environmental sciences-ecological processes, environmental exposure, and weather modeling. The paper briefly reviews hierarchical modeling specification, adopting a Bayesian perspective with full inference and associated uncertainty within the specification, while achieving exact inference to avoid what may be uncomfortable asymptotics. It focuses on point-referenced (geo-statistical) and point pattern spatial settings. It looks in some detail at problems involving data fusion, species distributions, and large spatial datasets. It also briefly describes four further examples arising from the author's recent research projects. (C) 2012 Elsevier B.V. All rights reserved.
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