Conjugate priors and bias reduction for logistic regression models

Tommaso Rigon and Emanuele Aliverti

Statistics & Probability Letters, 2023, vol. 202, issue C

Abstract: We address the issue of divergent maximum likelihood estimates for logistic regression models by considering a conjugate prior penalty which always produces finite estimates. We show that the proposed method is closely related to the reduced-bias approach of Firth (1993), and that the induced penalized likelihood can be expressed as a genuine binomial likelihood, replacing the original data with pseudo-counts.

Keywords: Bias reduction; Boundary estimate; Conjugate prior; Exponential family; Pseudo-counts (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.spl.2023.109901

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