Estimating equations for spatially correlated data in multi-dimensional space

• 成果类型: Article
• 署名作者: Lin, Pei-Sheng
• 署名单位: National Chung Cheng University; National Health Research Institutes - Taiwan
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asn046
• 发表日期: 2008
• 页码: 847858
• 关键词: binary data; REGRESSION-MODEL; EFFICIENCY

摘要

We use the quasilikelihood concept to propose an estimating equation for spatial data with correlation across the study region in a multi-dimensional space. With appropriate mixing conditions, we develop a central limit theorem for a random field under various L-p metrics. The consistency and asymptotic normality of quasilikelihood estimators can then be derived. We also conduct simulations to evaluate the performance of the proposed estimating equation, and a dataset from East Lansing Woods is used to illustrate the method.