文献类型：期刊文献

英文题名：A spatial model for rare binary events

作者：Morris, Samuel A.[1] Reich, Brian J.[2] Pacifici, Krishna[2] Lei, Yuancai[3]

第一作者：Morris, Samuel A.

通信作者：Reich, BJ[1]

机构：[1]Google Inc, Mountain View, CA USA;[2]North Carolina State Univ, Raleigh, NC 27695 USA;[3]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China

年份：2017

卷号：24

期号：4

起止页码：485-504

外文期刊名：ENVIRONMENTAL AND ECOLOGICAL STATISTICS

收录：;Scopus(收录号：2-s2.0-85032675915);WOS:【SCI-EXPANDED(收录号:WOS:000416331200002)】；

基金：The authors thank to the Ministry of Science and Technology and National Natural Sciences Foundation of China for financial support of the field work (Project Research Grants 2005DIB5JI42, 31170588 and 30510103195).

语种：英文

外文关键词：Ecology; Extreme value analysis; Generalized linear model; Max-stable process; Occupancy

摘要：Many predominant spatial methods for binary data use a latent Gaussian process to capture spatial dependence. However, this may not be appropriate for rare data because these methods based on Gaussian processes are asymptotically independent as the event probability goes to zero. In this paper, we propose a method for rare binary data that builds on spatial extreme value theory. We model binary events as exceedances of a max-stable process and show that this construction maintains spatial dependence even as the event probability goes to zero. We compare our model to spatial probit and logistic methods through a simulation study and analysis of a survey of Tamarix ramosissima and Hedysarum scoparium. We find some evidence that for very rare data the max-stable extension provides an improvement in spatial prediction compared to Gaussian models.