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A link-free sparse group variable selection method for single-index model

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  • Bilin Zeng
  • Xuerong Meggie Wen
  • Lixing Zhu

Abstract

For regression problems with grouped covariates, we adapt the idea of sparse group lasso (SGL) [10] to the framework of the sufficient dimension reduction. Assuming that the regression falls into a single-index structure, we propose a method called the sparse group sufficient dimension reduction to conduct group and within-group variable selections simultaneously without assuming a specific link function. Simulation studies show that our method is comparable to the SGL under the regular linear model setting and outperforms SGL with higher true positive rates and substantially lower false positive rates when the regression function is nonlinear. One immediate application of our method is to the gene pathway data analysis where genes naturally fall into groups (pathways). An analysis of a glioblastoma microarray data is included for illustration of our method.

Suggested Citation

  • Bilin Zeng & Xuerong Meggie Wen & Lixing Zhu, 2017. "A link-free sparse group variable selection method for single-index model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2388-2400, October.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2388-2400
    DOI: 10.1080/02664763.2016.1254731
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    Cited by:

    1. Huang Hailin & Shangguan Jizi & Ruan Peifeng & Liang Hua, 2019. "Bi-level feature selection in high dimensional AFT models with applications to a genomic study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(5), pages 1-11, October.

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