Author(s)

Georges Nguefack-Tsague¹, Walter Zucchini²

¹Biostatistics Unit, Department of Public Health, Faculty of Medicine and Biomedical Sciences, University of Yaounde 1, Yaounde, Cameroon.
²Institute for Statistics and Econometrics, University of Goettingen, Goettingen, Germany.

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.

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

Nguefack-Tsague, G. and Zucchini, W. (2016) A Mixture-Based Bayesian Model Averaging Method. Open Journal of Statistics, 6, 220-228. doi: 10.4236/ojs.2016.62019.