Estimating the Probability of Rare Events Occurring Using a Local Model Averaging

Jin-Hua Chen, Chun-Shu Chen, Meng-Fan Huang, Hung-Chih Lin

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.
Original languageEnglish
Pages (from-to)n/a-n/a
JournalRisk Analysis
DOIs
Publication statusPublished - 2016

Keywords

  • Kullback-Leibler loss, logistic regression, maximum likelihood estimate, uncertainty

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