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Benefits of dimension reduction in penalized regression methods for high dimensional grouped data: a case study in low sample size

Abstract : Motivation: In some prediction analyses, predictors have a natural grouping structure and selecting predictors accounting for this additional information could be more effective for predicting the outcome accurately. Moreover, in a high dimension low sample size framework, obtaining a good predictive model becomes very challenging. The objective of this work was to investigate the benefits of dimension reduction in penalized regression methods, in terms of prediction performance and variable selection consistency, in high dimension low sample size data. Using two real data-sets, we compared the performances of lasso, elastic net, group lasso, sparse group lasso, sparse partial least squares (PLS), group PLS and sparse group PLS. Results: Considering dimension reduction in penalized regression methods improved the prediction accuracy. The sparse group PLS reached the lowest prediction error while consistently selecting a few predictors from a single group. Availability and implementation: R codes for the prediction methods are freely available at https:// github.com/SoufianeAjana/Blisar.
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https://hal.inria.fr/hal-02425449
Contributor : Boris Hejblum <>
Submitted on : Monday, January 6, 2020 - 12:04:45 PM
Last modification on : Tuesday, March 23, 2021 - 10:42:04 AM
Long-term archiving on: : Tuesday, April 7, 2020 - 7:33:31 PM

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Soufiane Ajana, Niyazi Acar, Lionel Brétillon, Boris Hejblum, Hélène Jacqmin-Gadda, et al.. Benefits of dimension reduction in penalized regression methods for high dimensional grouped data: a case study in low sample size. Bioinformatics, Oxford University Press (OUP), 2019, 35, pp.3628-3634. ⟨10.1093/bioinformatics/btz135⟩. ⟨hal-02425449⟩

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