A Lasso-Type Approach for Estimation and Variable Selection in Single Index Models

作者：Zeng Peng^*; He Tianhong; Zhu Yu

来源：Journal of Computational and Graphical Statistics, 2012, 21(1): 92-109.

DOI：10.1198/jcgs.2011.09156

• Local linear smoothing
• m-fold cross-validation
• Solution path

The single index model is a natural extension of the linear regression model for applications in which linearity does not hold. In this article, we propose a penalized local linear smoothing method, called sim-lasso, for estimation and variable selection under the single index model. The sim-lasso method penalizes the derivative of the link function and thus can be considered an extension of the usual lasso. Computational algorithms are developed for calculating the sim-lasso estimates and solution paths. The properties of the solution paths are also investigated. Simulation study and real data application demonstrate the excellent performance of the sim-lasso method. Supplemental materials for the article are available online.

• 出版日期2012-3