Solutions to problems of nonexistence of parameter estimates and sparse data bias in Poisson regression.

ABSTRACT: Poisson regression can be challenging with sparse data, in particular with certain data constellations where maximum likelihood estimates of regression coefficients do not exist. This paper provides a comprehensive evaluation of methods that give finite regression coefficients when maximum likelihood estimates do not exist, including Firth's general approach to bias reduction, exact conditional Poisson regression, and a Bayesian estimator using weakly informative priors that can be obtained via data augmentation. Furthermore, we include in our evaluation a new proposal for a modification of Firth's approach, improving its performance for predictions without compromising its attractive bias-correcting properties for regression coefficients. We illustrate the issue of the nonexistence of max

SUBMITTER: Joshi A 

PROVIDER: S-EPMC8829730 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature