A combined estimating function approach for fitting stationary point process models

• 成果类型: Article
• 署名作者: Deng, C.; Waagepetersen, R. P.; Guan, Y.
• 署名单位: Yale University; Aalborg University; University of Miami
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ast069
• 发表日期: 2014
• 页码: 393408
• 关键词: estimating equations; inference; selection

摘要

A composite likelihood technique based on pairwise contributions provides a computationally simple but potentially inefficient approach for fitting spatial point process models. We propose a new estimation procedure that improves the efficiency. Our approach combines estimating functions derived from pairwise composite likelihood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate the efficacy of our proposed method through a simulation study and an application to the longleaf pine data.