A high-dimensional focused information criterion

Thomas Gueuning and Gerda Claeskens

No 582649, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

Abstract: The focused information criterion for model selection is constructed to select the model that best estimates a particular quantity of interest, the focus, in terms of mean squared error. We extend this focused selection process to the high-dimensional regression setting with potentially a larger number of parameters than the size of the sample. We distinguish two cases: (i) the case where the considered submodel is of low-dimension and (ii) the case where it is of high-dimension. In the former case, we obtain an alternative expression of the low-dimensional focused information criterion that can directly be applied. In the latter case we use a desparsified estimator that allows us to derive the mean squared error of the focus estimator. We illustrate the performance of the high-dimensional focused information criterion with a numerical study and a real dataset.

Keywords: Desparsified estimator; Focused information criterion; High-dimensional data; Variable selection (search for similar items in EconPapers)
Date: 2017-05
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Published in FEB Research Report KBI_1706

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