On least squares fitting for stationary spatial point processes
成果类型：
Article
署名作者：
Guan, Yongtao; Sherman, Michael
署名单位：
Yale University; Texas A&M University System; Texas A&M University College Station
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
发表日期：
2007
页码：
31-49
关键词：
摘要：
The K-function is a popular tool for fitting spatial point process models owing to its simplicity and wide applicability. In this work we study the properties of least squares estimators of model parameters and propose a new method of model fitting via the K-function by using subsampling. We demonstrate consistency and asymptotic normality of our estimators of model parameters and compare the efficiency of our procedure with existing procedures. This is done through asymptotic theory, simulation experiments and an application to a data set on long leaf pine-trees.