Free access to SJS paper: 'A High‐dimensional Focused Information Criterion'


  • Author: Statistics Views
  • Date: 11 June 2018

Each week, we select a recently published article and provide free access. This week's is from the Scandinavian Journal of Statistics and is available from the March 2018 issue.

A High‐dimensional Focused Information Criterion

Thomas Gueuning and Gerda Claeskens

Scandinavian Journal of Statistics, Volume 45, Issue 1, March 2018, pages 34-61


The introduction is provided below:

thumbnail image: Free access to SJS paper: 'A High‐dimensional Focused Information Criterion'

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.

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