Overdispersion Diagnostics for Generalized Linear Models
针对计数数据常用的广义线性模型，提出凸性图、相对方差曲线和检验，用于检测和区分过度离散的来源，比传统得分检验更有效。
Abstract Generalized linear models (GLM's) are simple, convenient models for count data, but they assume that the variance is a specified function of the mean. Although overdispersed GLM's allow more flexible mean-variance relationships, they are often not as simple to interpret nor as easy to fit as standard GLM's. This article introduces a convexity plot, or C plot for short, that detects overdispersion and relative variance curves and relative variance tests that help to understand the nature of the overdispersion. Convexity plots sometimes detect overdispersion better than score tests, and relative variance curves and tests sometimes distinguish the source of the overdispersion better than score tests.