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Burnham, K.P. and Anderson, D.R. (2013) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer, Cambridge.

has been cited by the following article:

• TITLE: A Mixture-Based Bayesian Model Averaging Method

  AUTHORS: Georges Nguefack-Tsague, Walter Zucchini

  KEYWORDS: Mixture, Bayesian Model Selection, Bayesian Model Averaging, Bayesian Theory, Frequentist Performance

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.2, April 21, 2016

  ABSTRACT: 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.