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与齐次泊松过程相伴

Sojourning with the Homogeneous Poisson Process.

作者信息

Liu Piaomu, Peña Edsel A

机构信息

PhD student, Department of Statistics, University of South Carolina, Columbia, SC 29208.

Professor, Department of Statistics, University of South Carolina, Columbia, SC 29208.

出版信息

Am Stat. 2016;70(4):413-423. doi: 10.1080/00031305.2016.1200484. Epub 2014 Jun 1.

Abstract

In this pedagogical article, distributional properties, some surprising, pertaining to the homogeneous Poisson process (HPP), when observed over a possibly random window, are presented. Properties of the gap-time that covered the termination time and the correlations among gap-times of the observed events are obtained. Inference procedures, such as estimation and model validation, based on event occurrence data over the observation window, are also presented. We envision that through the results in this paper, a better appreciation of the subtleties involved in the modeling and analysis of recurrent events data will ensue, since the HPP is arguably one of the simplest among recurrent event models. In addition, the use of the theorem of total probability, Bayes theorem, the iterated rules of expectation, variance and covariance, and the renewal equation could be illustrative when teaching distribution theory, mathematical statistics, and stochastic processes at both the undergraduate and graduate levels. This article is targeted towards both instructors and students.

摘要

在这篇教学文章中,给出了关于齐次泊松过程(HPP)在可能的随机窗口上观察时的分布特性,其中一些特性令人惊讶。得到了覆盖终止时间的间隔时间的性质以及观察到的事件的间隔时间之间的相关性。还给出了基于观察窗口上的事件发生数据的推断程序,如估计和模型验证。我们设想,通过本文的结果,将能更好地理解复发事件数据建模和分析中所涉及的微妙之处,因为HPP可以说是复发事件模型中最简单的模型之一。此外,在本科和研究生阶段教授分布理论、数理统计和随机过程时,使用全概率定理、贝叶斯定理、期望、方差和协方差的迭代规则以及更新方程可能会有启发作用。本文面向教师和学生。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/79c4/5665405/201dd7da3476/nihms876707f1.jpg

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本文引用的文献

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Nonparametric Estimation of a Recurrent Survival Function.复发生存函数的非参数估计
J Am Stat Assoc. 1999 Mar 1;94(445):146-153. doi: 10.1080/01621459.1999.10473831.

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