Model Selection and Estimation in Regression with Grouped Variables

Dados Bibliográficos

ID23383100
AutoresMing Yuan (0000-0002-8516-392X, Georgia Institute of Technology, autor correspondente), Yi Lin (0000-0002-0697-4581, University of Wisconsin–Madison)
Ano2006
Volume68
Fascículo1
Páginas49-67
Data de publicação2006-02-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoJournal of the Royal Statistical Society Series B: Statistical Methodology (JOURNAL)
Identificadores do periódicoISSN: 1369-7412 • E-ISSN: 1467-9868
EditoraOxford University Press (OUP) (PUBLISHER)
DOI10.1111/j.1467-9868.2005.00532.x
OpenAlexW2138019504
IdiomaEN
Citações recebidas62
Referências citadas9

We consider the problem of selecting grouped variables (factors) for accurate prediction in regression. Such a problem arises naturally in many practical situations with the multifactor analysis-of-variance problem as the most important and well-known example. Instead of selecting factors by stepwise backward elimination, we focus on the accuracy of estimation and consider extensions of the lasso, the LARS algorithm and the non-negative garrotte for factor selection. The lasso, the LARS algorithm and the non-negative garrotte are recently proposed regression methods that can be used to select individual variables. We study and propose efficient algorithms for the extensions of these methods for factor selection and show that these extensions give superior performance to the traditional stepwise backward elimination method in factor selection problems. We study the similarities and the differences between these methods. Simulations and real examples are used to illustrate the methods.

Estimation · Feature selection · Focus (optics) · Lasso (programming language) · Machine learning · Regression · Regression analysis · Selection (genetic algorithm) · Statistics · Stepwise regression · Variance (accounting) · Advanced Statistical Methods and Models · Artificial Intelligence · Computer Science · Control Systems and Identification · Engineering · Mathematics · Statistical Methods and Inference