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

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Joshi , A , Geroldinger , A , Jiricka , L , Senchaudhuri , P , Corcoran , C & Heinze , G 2022 , ' Solutions to problems of nonexistence of parameter estimates and sparse data bias in Poisson regression ' , Statistical Methods in Medical Research , vol. 31 , no. 2 , 09622802211065405 , pp. 253–266 . https://doi.org/10.1177/09622802211065405

Title: Solutions to problems of nonexistence of parameter estimates and sparse data bias in Poisson regression
Author: Joshi, Ashwini; Geroldinger, Angelika; Jiricka, Lena; Senchaudhuri, Pralay; Corcoran, Christopher; Heinze, Georg
Contributor organization: Department of Mathematics and Statistics
Date: 2022-02-01
Language: eng
Number of pages: 14
Belongs to series: Statistical Methods in Medical Research
ISSN: 0962-2802
DOI: https://doi.org/10.1177/09622802211065405
URI: http://hdl.handle.net/10138/340417
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 maximum likelihood estimates with a dataset arising from the recent outbreak of COVID-19 and an example from implant dentistry. All methods are evaluated in a comprehensive simulation study under a variety of realistic scenarios, evaluating their performance for prediction and estimation. To conclude, while exact conditional Poisson regression may be confined to small data sets only, both the modification of Firth's approach and the Bayesian estimator are universally applicable solutions with attractive properties for prediction and estimation. While the Bayesian method needs specification of prior variances for the regression coefficients, the modified Firth approach does not require any user input.
Subject: 3141 Health care science
113 Computer and information sciences
111 Mathematics
Count data
Firth's penalization
generalized linear models
penalized regression
Poisson regression
separation
LOGISTIC-REGRESSION
LIKELIHOOD
SEPARATION
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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