Revisiting the analysis pipeline for overdispersed Poisson and binomial data.

ABSTRACT: Overdispersion is a common feature in categorical data analysis and several methods have been developed for detecting and handling it in generalized linear models. The first aim of this study is to clarify the relationships among various score statistics for testing overdispersion and to compare their performances. In addition, we investigate a principled way to correct finite sample bias in the score statistic caused by estimating regression parameters with restricted likelihood. The second aim is to reconsider the current practice for handling overdispersed categorical data. Although the conventional models are based on substantially different mechanisms for generating overdispersion, model selection in practice has not been well studied. We perform an intensive numerical study for deter

SUBMITTER: Lee W 

PROVIDER: S-EPMC10184615 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature