Nonparametric Maximum Likelihood Estimation of Features in Spatial Point Processes Using Voronoï Tessellation
Nonparametric Maximum Likelihood Estimation of Features in Spatial Point Processes Using Voronoï Tessellation
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DOI：
10.1080/01621459.1997.10473670
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发表时间：
1997-12
影响因子：
3.7
通讯作者：
D. Allard;C. Fraley
中科院分区：
文献类型：
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作者：
D. Allard;C. Fraley
摘要本文讨论了有界点过程在背景噪声存在下的支撑域估计问题。例如，在通过空中观察探测雷场时就出现了这种情况。均匀点过程的混合物的最大似然估计使用的自然分区的数据本身定义的空间：Voronoi镶嵌。该方法进行了测试模拟和基于模型的聚类技术相比。
Abstract 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.