Variable selection via the weighted group lasso for factor analysis models

Kei Hirose, Sadanori Konishi

研究成果: Contribution to journalArticle

13 引用 (Scopus)

抜粋

We consider the problem of selecting variables in factor analysis models. The L 1 regularization procedure is introduced to perform an automatic variable selection. In the factor analysis model, each variable is controlled by multiple factors when there are more than one underlying factor. We treat parameters corresponding to the multiple factors as grouped parameters, and then apply the group lasso. Furthermore, the weight of the group lasso penalty is modified to obtain appropriate estimates and improve the performance of variable selection. Crucial issues in this modeling procedure include the selection of the number of factors and a regularization parameter. Choosing these parameters can be viewed as a model selection and evaluation problem. We derive a model selection criterion for evaluating the factor analysis model via the weighted group lasso. Monte Carlo simulations are conducted to investigate the effectiveness of the proposed procedure. A real data example is also given to illustrate our procedure.

元の言語英語
ページ(範囲)345-361
ページ数17
ジャーナルCanadian Journal of Statistics
40
発行部数2
DOI
出版物ステータス出版済み - 6 1 2012
外部発表Yes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

フィンガープリント Variable selection via the weighted group lasso for factor analysis models' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用