A Review on Variable Selection in Regression Analysis

Loann Desboulets

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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

Keywords: automatic modelling; variable selection; sparse models (search for similar items in EconPapers)
Date: 2018-11
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-01954386
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Citations: View citations in EconPapers (12)

Published in Econometrics, 2018, 6 (4), pp.45. ⟨10.3390/econometrics6040045⟩

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Journal Article: A Review on Variable Selection in Regression Analysis (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01954386

DOI: 10.3390/econometrics6040045

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