文章基本信息

标题：Frequentist Model Averaging and Applications to Bernoulli Trials
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作者：Georges Nguefack-Tsague ; Walter Zucchini ; Siméon Fotso 等
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2016
卷号：06
期号：03
页码：545-553
DOI：10.4236/ojs.2016.63046
语种：English
出版社：Scientific Research Publishing
摘要：In several instances of statistical practice, it is not uncommon to use the same data for both model selection and inference, without taking account of the variability induced by model selection step. This is usually referred to as post-model selection inference. The shortcomings of such practice are widely recognized, finding a general solution is extremely challenging. We propose a model averaging alternative consisting on taking into account model selection probability and the like-lihood in assigning the weights. The approach is applied to Bernoulli trials and outperforms Akaike weights model averaging and post-model selection estimators.
关键词：Model Selection;Post-Model Selection Estimator;Frequentist Model Averaging;Bernoulli Trials