Volume 6, Issue 1, March 2020, Page: 1-9
Variable Selection for Partially Linear Additive Model Based on Modal Regression Under High Dimensional Data
Yafeng Xia, School of Sciences, Lanzhou University of Technology, Lanzhou, P. R. China
Lirong Zhang, School of Sciences, Lanzhou University of Technology, Lanzhou, P. R. China
Received: Dec. 19, 2019;       Accepted: Jan. 9, 2020;       Published: Apr. 17, 2020
DOI: 10.11648/j.ijsd.20200601.11      View  281      Downloads  80
Abstract
In this article, we focus on the variable selection for partially linear additive model under high dimensional data. Variable selection is proposed based on modal regression estimation with Adoptive Bridge Method. Using the B-spline basic function to approximate the additive function, a penalty estimation objective equation is constructed. It establishes and proves that the variable selection methods have oracle property. Numerical simulations tested the performance of the proposed methods in a finite sample and verified the significance of the proposed estimation and the variable selection methods. At the end of the article, we attach the detailed derivation of the theoretical results. Therefore, the correctness of the method used is verified theoretically and practically.
Keywords
High Dimensional Data, Partially Linear Additive Model, Modal Regression, Variable Selection, Adoptive Bridge, B-spline
To cite this article
Yafeng Xia, Lirong Zhang, Variable Selection for Partially Linear Additive Model Based on Modal Regression Under High Dimensional Data, International Journal of Statistical Distributions and Applications. Vol. 6, No. 1, 2020, pp. 1-9. doi: 10.11648/j.ijsd.20200601.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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