A Disaggregate Negative Binomial Regression Procedure for Count Data Analysis
提出一种分组负二项回归方法，用于分析来自异质性总体的非负整数计数数据，通过E-M算法同时估计组比例、组内回归系数和过度离散程度，并以消费品购买数据为例说明。
Various research areas face the methodological problems presented by nonnegative integer count data drawn from heterogeneous populations. We present a disaggregate negative binomial regression procedure for analysis of count data observed for a heterogeneous sample of cross-sections, possibly over some fixed time periods. This procedure simultaneously pools or groups cross-sections while estimating a separate negative binomial regression model for each group. An E-M algorithm is described within a maximum likelihood framework to estimate the group proportions, the group-specific regression coefficients, and the degree of overdispersion in event rates within each derived group. The proposed procedure is illustrated with count data entailing nonnegative integer counts of purchases (events) for a frequently bought consumer good.