Treffer: Grouping Variable Selection by Weight Fused Elastic Net for Multi-Collinear Data
Title:
Grouping Variable Selection by Weight Fused Elastic Net for Multi-Collinear Data
Authors:
Source:
Communications in statistics. Simulation and computation. 41(1-2):205-221
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse corrélation, Correlation analysis, Análisis correlación, Estimation, Estimación, Méthode statistique, Statistical method, Método estadístico, Problème sélection, Selection problem, Problema selección, Simulation numérique, Numerical simulation, Simulación numérica, Méthode sélection, Selection method, Sélection variable, Variable selection, Elastic net, Grouping variable selection, LASSO, Multi-collinearity, Primary 62H86, Secondary 62-07, WFEN, Weight fused LASSO
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
School of Mathematical Science and Computing Technology, Central South University, Changsha, China
ISSN:
0361-0918
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.25576921
Database:
PASCAL Archive
Weitere Informationen
In this article, we consider the problem of variable selection and estimation with the strongly correlated multi-collinear data by using grouping variable selection techniques. A new grouping variable selection method, called weight-fused elastic net(WFEN), is proposed to deal with the high dimensional collinear data. The proposed model, combined two different grouping effect mechanisms induced by the elastic net and weight-fused LASSO, respectively, can be easily unified in the frame of LASSO and computed efficiently. The performance with the simulation and real data sets shows that our method is competitive with other related methods, especially when the data present high multi-collinearity.