Nonparametric maximum likelihood estimation of features in spatial point processes using Voronoi tessellation

• 成果类型: Article
• 署名作者: Allard, D; Fraley, C
• 署名单位: University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2965419
• 发表日期: 1997
• 页码: 1485-1493
• 关键词: poisson

摘要

This article addresses the problem of estimating the support domain of a bounded point process in presence of background noise. This situation occurs, for example, in the detection of a minefield from aerial observations. A maximum likelihood estimator for a mixture of uniform point processes is derived using a natural partition of the space defined by the data themselves: the Voronoi tessellation. The methodology is tested on simulations and compared to a model-based clustering technique.