Multivariate Gamma-Poisson Models
本文扩展了多元伽马-泊松模型，通过复合狄利克雷分布处理横截面数据，并允许个体率在纵向数据第二期变化，用于分析工作失能、监狱违规和犯罪受害等重复事件。
Abstract This article extends the multivariate gamma-Poisson model of repeated events, developed by Arbous and Kerrich (1951) and Bates and Neyman (1952), by (a) compounding it with a Dirichlet distribution in the analysis of cross-sectional data and by (b) allowing individual rates to shift at the start of the second period in the analysis of two-period longitudinal data. The first extension allows individuals to specialize in particular types of events. The model is shown to describe multivariate distributions of work incapacities, prison infractions, and criminal victimizations. The second extension provides a framework for estimating changes in individual rates. It is used to describe the shift in victimization rates observed in longitudinal studies of the National Crime Survey. Key Words: Dirichlet distributionGamma distributionRepeated events