A Kernel Method for Smoothing Point Process Data
A Kernel Method for Smoothing Point Process Data
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DOI：
10.2307/2347366
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发表时间：
1985-06
影响因子：
1.6
通讯作者：
P. Diggle
中科院分区：
文献类型：
--
作者：
P. Diggle
描述了一种估计一维点过程局部强度的方法。估计量使用Rosenblatt非参数概率密度估计的核方法的适应性，并对端效应进行校正。的均方误差的表达式推导出的假设下，基本过程是一个平稳的考克斯过程，这个结果是用来建议一个实用的方法选择的平滑常数的值。使用模拟数据的估计的性能进行说明。本文介绍了一种应用于沿沿着节理位置数据的方法。注意到了对二维点过程的扩展。
A method for estimating the local intensity of a one‐dimensional point process is described. The estimator uses an adaptation of Rosenblatt's kernel method of non‐parametric probability density estimation, with a correction for end‐effects. An expression for the mean squared error is derived on the assumption that the underlying process is a stationary Cox process, and this result is used to suggest a practical method for choosing the value of the smoothing constant. The performance of the estimator is illustrated using simulated data. An application to data on the locations of joints along a coal seam is described. The extension to two‐dimensional point processes is noted.