摘要： 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. 其他題名： Risk Analysis 出版者： United States: Blackwell Publishing Ltd 出版日期： 2016-10 出處： Risk analysis, 2016-10, Vol.36 (10), p.1855-1870 資源來源： EBSCOhost Business Source Premier 版權： 2016 Society for Risk Analysis 版權： 2016 Society for Risk Analysis. 識別號： ISSN: 0272-4332 識別號： ISSN: 1539-6924 識別號： EISSN: 1539-6924 識別號： DOI: 10.1111/risa.12558 識別號： PMID: 26857871
