Spline-Lasso in High-Dimensional Linear Regression

Jianhua Guo, Jianchang Hu, Bing-Yi Jing and Zhen Zhang

Journal of the American Statistical Association, 2016, vol. 111, issue 513, 288-297

Abstract: We consider a high-dimensional linear regression problem, where the covariates (features) are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n . The fused lasso of Tibshirani et al. is designed especially to tackle this type of problems; it yields sparse coefficients and selects grouped variables, and encourages local constant coefficient profile within each group. However, in some applications, the effects of different features within a group might be different and change smoothly. In this article, we propose a new spline-lasso or more generally, spline-MCP to better capture the different effects within the group. The newly proposed method is very easy to implement since it can be easily turned into a lasso or MCP problem. Simulations show that the method works very effectively both in feature selection and prediction accuracy. A real application is also given to illustrate the benefits of the method. Supplementary materials for this article are available online.

Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1080/01621459.2015.1005839

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