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In several instances of statisticalpractice, it is not uncommon to use the same data for both model selection andinference, without taking account of the variability induced by model selectionstep. This is usually referred to as post-model selection inference. Theshortcomings of such practice are widely recognized, finding a general solutionis extremely challenging. We propose a model averaging alternative consistingon taking into account model selection probability and the like-lihood inassigning the weights. The approach is applied to Bernoulli trials andoutperforms Akaike weights model averaging and post-model selection estimators.

KEYWORDS

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

Cite this paper

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.





Author: Georges Nguefack-Tsague1, Walter Zucchini2, Siméon Fotso3

Source: http://www.scirp.org/



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