HETEROGENEITY, EXCESS ZEROS, AND THE STRUCTURE OF COUNT DATA MODELS
证明计数数据模型中常见的未观测异质性必然导致过多零值，并据此提出检验统计量，在医疗利用数据中拒绝泊松模型而支持混合模型。
This paper demonstrates that the unobserved heterogeneity commonly assumed to be the source of overdispersion in count data models has predictable implications for the probability structure of such mixture models. In particular, the common observation of excess zeros is a strict implication of unobserved heterogeneity. This result has important implications for using count model estimates for predicting certain interesting parameters. Test statistics to detect such heterogeneity-related departures from the null model are proposed and applied in a health-care utilization example, suggesting that a null Poisson model should be rejected in favour of a mixed alternative. © 1997 John Wiley & Sons, Ltd.