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Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty.  
When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference). In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.


James and Stein estimator model selection model averaging minimax normal multivariate mean

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How to Cite
Nguefack-Tsague, G. (2014). Estimation of a multivariate mean under model selection uncertainty. Pakistan Journal of Statistics and Operation Research, 10(1), 131-145.