On semiparametric inference of geostatistical models via local Karhunen-Loeve expansion

• 成果类型: Article
• 署名作者: Chu, Tingjin; Wang, Haonan; Zhu, Jun
• 署名单位: Renmin University of China; Colorado State University System; Colorado State University Fort Collins; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12053
• 发表日期: 2014
• 页码: 817-832
• 关键词: asymptotic properties; estimators

摘要

We develop a semiparametric approach to geostatistical modelling and inference. In particular, we consider a geostatistical model with additive components, where the form of the covariance function of the spatial random error is not prespecified and thus is flexible. A novel, local Karhunen-Loeve expansion is developed and a likelihood-based method is devised for estimating the model parameters and statistical inference. A simulation study demonstrates sound finite sample properties and a real data example is given for illustration. Finally, the theoretical properties of the estimates are explored and, in particular, consistency results are established.