Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size.
Bioinformatics
; 35(19): 3628-3634, 2019 10 01.
Article
en En
| MEDLINE
| ID: mdl-30931473
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 datasets, 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. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Tamaño de la Muestra
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Francia