Variable selection via the weighted group lasso for factor analysis models

Kei Hirose, Sadanori Konishi

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)345-361
Number of pages17
JournalCanadian Journal of Statistics
Issue number2
Publication statusPublished - Jun 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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