Variable selection via the weighted group lasso for factor analysis models

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<jats:title>Abstract</jats:title><jats:p>We consider the problem of selecting variables in factor analysis models. The<jats:styled-content>$L_1$<jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/tex2gif-ueqn-1.gif" xlink:title="equation image"/></jats:styled-content>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.<jats:italic>The Canadian Journal of Statistics</jats:italic>40: 345–361; 2012 © 2012 Statistical Society of Canada</jats:p>

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