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A Review on Variable Selection in Regression Analysis

Abstract : In this paper, we investigate on 39 Variable Selection procedures to give an overview of the existing 1 literature for practitioners. "Let the data speak for themselves" has become the motto of many applied researchers 2 since the amount of data has significantly grew. Automatic model selection have been raised by the search 3 for data-driven theories for quite a long time now. However while great extensions have been made on the 4 theoretical side still basic procedures are used in most empirical work, eg. Stepwise Regression. Some reviews 5 are already available in the literature for variable selection, but always focus on a specific topic like linear 6 regression, groups of variables or smoothly varying coefficients. Here we provide a review of main methods and 7 state-of-the art extensions as well as a topology of them over a wide range of model structures (linear, grouped, 8 additive, partially linear and non-parametric). We provide explanations for which methods to use for different 9 model purposes and what are key differences among them. We also review two methods for improving variable 10 selection in the general sense. 11
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https://hal-amu.archives-ouvertes.fr/hal-01812707
Contributor : Loann Desboulets <>
Submitted on : Monday, June 11, 2018 - 4:50:20 PM
Last modification on : Wednesday, August 5, 2020 - 3:14:44 AM
Long-term archiving on: : Wednesday, September 12, 2018 - 6:25:57 PM

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Loann Desboulets. A Review on Variable Selection in Regression Analysis. 2018. ⟨hal-01812707⟩

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