On selection of spatial linear models for lattice data

• 成果类型: Article
• 署名作者: Zhu, Jun; Huang, Hsin-Cheng; Reyes, Perla E.
• 署名单位: Colorado State University System; Colorado State University Fort Collins; University of Wisconsin System; University of Wisconsin Madison; Academia Sinica - Taiwan; National Yang Ming Chiao Tung University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2010.00739.x
• 发表日期: 2010
• 页码: 389-402
• 关键词: nonconcave penalized likelihood; least angle; regression; shrinkage

摘要

Spatial linear models are popular for the analysis of data on a spatial lattice, but statistical techniques for selection of covariates and a neighbourhood structure are limited. Here we develop new methodology for simultaneous model selection and parameter estimation via penalized maximum likelihood under a spatial adaptive lasso. A computationally efficient algorithm is devised for obtaining approximate penalized maximum likelihood estimates. Asymptotic properties of penalized maximum likelihood estimates and their approximations are established. A simulation study shows that the method proposed has sound finite sample properties and, for illustration, we analyse an ecological data set in western Canada.