Variable Selection of Partially Linear Single-index Models

doi:10.13371/j.cnki.chin.q.j.m.2014.03.009

Chinese Quarterly Journal of Mathematics ›› 2014, Vol. 29 ›› Issue (3): 392-399.doi: 10.13371/j.cnki.chin.q.j.m.2014.03.009

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Variable Selection of Partially Linear Single-index Models

Institute of Cryptography and Engineering, the PLA Information Engineering University

Received:2013-04-24 Online:2014-09-30 Published:2020-11-30
About author:LU Yi-qiang(1971-), male, native of Jiyuan, Henan, a professor of the PLA Information Engineering University, Ph.D., engages in the mathematical statistics.
Supported by:
Supported by the NNSF of China(61272041)；

Abstract

Abstract: In this article, we study the variable selection of partially linear single-index model（PLSIM）. Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.

Key words: variable selection, adaptive LASSO, minimized average variance estimation(MAVE), partially linear single-index model

CLC Number:

O212

LU Yi-qiang, HU Bin. Variable Selection of Partially Linear Single-index Models[J]. Chinese Quarterly Journal of Mathematics, 2014, 29(3): 392-399.

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Variable Selection of Partially Linear Single-index Models

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