Models for Bivariate Count Data: Bivariate Poisson Distribution
Models for Bivariate Count Data: Bivariate Poisson Distribution
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双变量计数数据模型：双变量泊松分布
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
R. Chowdhury
中科院分区：
文献类型：
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作者：
M. Islam;R. Chowdhury
在健康科学、交通事故、经济学、精算学、社会科学、环境研究等领域的许多情况下，都可以观察到计数结果变量的依赖性。这种依赖性的一个典型例子出现在交通事故中，其中身体伤害的程度可能导致死亡。双变量泊松分布已经开发了各种假设。在本章中，我们讨论了几个二元Poisson模型，包括Poisson-Poisson的二元GLM模型，广义零截尾二元Poisson模型，右截尾二元Poisson模型和二元双Poisson模型。给出了分析二元计数数据的广义线性模型，并讨论了超离差或欠离差问题。本章还重点介绍了双变量计数数据的截断问题。过度或欠分散的测试，以及测试的拟合优度的例子说明。
The dependence in the count outcome variables is observed in many instances in the fields of health sciences, traffic accidents, economics, actuarial science, social sciences, environmental studies, etc. A typical example of such dependence arises in the traffic accidents where the extent of physical injuries may lead to fatalities. The bivariate Poisson distribution has been developed following various assumptions. In this chapter, several bivariate Poisson models including bivariate GLM for Poisson–Poisson, generalized zero-truncated bivariate Poisson, right-truncated bivariate Poisson, and bivariate double Poisson are discussed. The generalized linear models are shown for analyzing bivariate count data and the over- or underdispersion problems are also discussed. The problem of truncation for bivariate count data is also highlighted in this chapter. Tests for over- or underdispersion as well as tests for goodness of fit are illustrated with examples.