6533b7d1fe1ef96bd125c285
RESEARCH PRODUCT
Inferential tools in penalized logistic regression for small and sparse data: A comparative study.
Marianna SiinoVito Mr MuggeoSalvatore Fasolasubject
Statistics and ProbabilityScore testPRESS statisticEpidemiologyStatistics as TopicScoreWald testLogistic regression01 natural sciences010104 statistics & probability03 medical and health sciences0302 clinical medicineHealth Information ManagementStatisticsEconometricsHumans030212 general & internal medicine0101 mathematicsStatisticMathematicsLikelihood FunctionsModels StatisticalLogistic regression firth penalized likelihood sandwich formula score statistic gradient statisticLogistic ModelsLikelihood-ratio testData Interpretation StatisticalSample SizeAncillary statisticSettore SECS-S/01 - Statisticadescription
This paper focuses on inferential tools in the logistic regression model fitted by the Firth penalized likelihood. In this context, the Likelihood Ratio statistic is often reported to be the preferred choice as compared to the ‘traditional’ Wald statistic. In this work, we consider and discuss a wider range of test statistics, including the robust Wald, the Score, and the recently proposed Gradient statistic. We compare all these asymptotically equivalent statistics in terms of interval estimation and hypothesis testing via simulation experiments and analyses of two real datasets. We find out that the Likelihood Ratio statistic does not appear the best inferential device in the Firth penalized logistic regression.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-08-12 | Statistical methods in medical research |