Author(s)

Georges Nguefack-Tsague¹, Walter Zucchini², Siméon Fotso³

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.
³Department of Mathematics, Higher Teachers’ Training College, University of Yaoundé I, Yaoundé, Cameroon.

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.

Model Selection, Post-Model Selection Estimator, Frequentist Model Averaging, Bernoulli Trials

Nguefack-Tsague, G. , Zucchini, W. and Fotso, S. (2016) Frequentist Model Averaging and Applications to Bernoulli Trials. Open Journal of Statistics, 6, 545-553. doi: 10.4236/ojs.2016.63046.