Article citationsMore>>

Simmons, S.J., Chen, C., Li, X., Wang, Y., Piegorsch, W.W., Fang, Q., Hu, B. and Dunn, G.E. (2015) Bayesian Model Averaging for Benchmark Dose Estimation. Environmental and Ecological Statistics, 22, 5-16.
http://dx.doi.org/10.1007/s10651-014-0285-4

has been cited by the following article:

• TITLE: Effects of Bayesian Model Selection on Frequentist Performances: An Alternative Approach

  AUTHORS: Georges Nguefack-Tsague, Walter Zucchini

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

  JOURNAL NAME: Applied Mathematics, Vol.7 No.10, June 22, 2016

  ABSTRACT: It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance; i.e. ignoring model selection uncertainty. The resulted estimator is called post-model selection estimator (PMSE) whose properties are hard to derive. Conditioning on data at hand (as it is usually the case), Bayesian model selection is free of this phenomenon. This paper is concerned with the properties of Bayesian estimator obtained after model selection when the frequentist (long run) performances of the resulted Bayesian estimator are of interest. The proposed method, using Bayesian decision theory, is based on the well known Bayesian model averaging (BMA)’s machinery; and outperforms PMSE and BMA. It is shown that if the unconditional model selection probability is equal to model prior, then the proposed approach reduces BMA. The method is illustrated using Bernoulli trials.