Identification and estimation of superposed Neyman-Scott spatial cluster processes
Identification and estimation of superposed Neyman-Scott spatial cluster processes
复制标题
叠加的 Neyman-Scott 空间聚类过程的识别和估计
DOI：
10.1007/s10463-013-0431-z
复制
发表时间：
2014
影响因子：
1
通讯作者：
Y.
中科院分区：
文献类型：
--
作者：
Tanaka;U. and Ogata;Y.
本文提出了一种不同距离尺度和聚类大小的Neyman-Scott聚类过程的叠加空间点模式的估计方法。与普通的单Neyman-Scott模型不同，Neyman-Scott模型的叠加过程不能仅由过程的二阶矩性质来识别。为了解决识别问题，我们使用最近邻距离属性，除了二阶矩属性。在本程序中，我们结合联合收割机的非齐次泊松似然的基础上的棕榈强度与另一个似然函数的基础上最近的邻居属性。将最近邻距离函数的导数作为旋转不变非齐次Poisson点过程的强度函数。目前的估计程序适用于两套生态定位数据。
This paper proposes an estimation method for superposed spatial point patterns of Neyman–Scott cluster processes of different distance scales and cluster sizes. Unlike the ordinary single Neyman–Scott model, the superposed process of Neyman–Scott models is not identified solely by the second-order moment property of the process. To solve the identification problem, we use the nearest neighbor distance property in addition to the second-order moment property. In the present procedure, we combine an inhomogeneous Poisson likelihood based on the Palm intensity with another likelihood function based on the nearest neighbor property. The derivative of the nearest neighbor distance function is regarded as the intensity function of the rotation invariant inhomogeneous Poisson point process. The present estimation procedure is applied to two sets of ecological location data.