Variable selection for partially varying coefficient model based on modal regression under high dimensional data
Yafeng Xia,
Lirong Zhang and
Aiping Zhang
Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 1, 232-248
Abstract:
In this article, we focus on the variable selection for partially varying coefficient model under high dimensional data. Variable selection is proposed based on modal regression estimation with bridge method. Using the B-spline basic function to approximate the non parametric 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.
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1747081 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:51:y:2022:i:1:p:232-248
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2020.1747081
Access Statistics for this article
Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe
More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().