文章基本信息

标题：A Mixture-Based Bayesian Model Averaging Method
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作者：Georges Nguefack-Tsague ; Walter Zucchini
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：02
页码：220-228
DOI：10.4236/ojs.2016.62019
语种：English
出版社：Scientific Research Publishing
摘要：Bayesian model averaging (BMA) is a popular and powerful statistical method of taking account of uncertainty about model form or assumption. Usually the long run (frequentist) performances of the resulted estimator are hard to derive. This paper proposes a mixture of priors and sampling distributions as a basic of a Bayes estimator. The frequentist properties of the new Bayes estimator are automatically derived from Bayesian decision theory. It is shown that if all competing models have the same parametric form, the new Bayes estimator reduces to BMA estimator. The method is applied to the daily exchange rate Euro to US Dollar.
关键词：Mixture;Bayesian Model Selection;Bayesian Model Averaging;Bayesian Theory;Frequentist Performance