Penalized logistic regression for small or sparse data: interval estimators revisited

Research output: Contribution to conferenceOtherpeer-review

Abstract

This paper focuses on interval estimation in logistic regression modelsfitted through the Firth penalized log-likelihood. In this context, many authorshave claimed superiority of the Likelihood ratio statistic with respect to the(wrong) Wald statistic via simulation evidence. We re-assess such findings bydetailing the inferential tools also including in the comparisons the (right) Waldstatistic and other statistics neglected in previous literature. In particular, weassess performances of the CIs estimators by simulation and compare them in areal data set. Differently from previous findings, the Likelihood ratio statistic doesnot appear to be the best inferential device in Firth penalized logistic regression.
Original languageEnglish
Number of pages4
Publication statusPublished - 2015

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