NDLI: Robust spline-based variable selection in varying coefficient model

Please wait, while we are loading the content...

Robust spline-based variable selection in varying coefficient model

Content Provider	Springer Nature Link
Author	Feng, Long Zou, Changliang Wang, Zhaojun Wei, Xianwu Chen, Bin
Copyright Year	2014
Abstract	The varying coefficient model is widely used as an extension of the linear regression model. Many procedures have been developed for the model estimation, and recently efficient variable selection procedures for the varying coefficient model have been proposed as well. However, those variable selection approaches are mainly built on the least-squares (LS) type method. Although the LS method is a successful and standard choice in the varying coefficient model fitting and variable selection, it may suffer when the errors follow a heavy-tailed distribution or in the presence of outliers. To overcome this issue, we start by developing a novel robust estimator, termed rank-based spline estimator, which combines the ideas of rank inference and polynomial spline. Furthermore, we propose a robust variable selection method, incorporating the smoothly clipped absolute deviation penalty into the rank-based spline loss function. Under mild conditions, we theoretically show that the proposed rank-based spline estimator is highly efficient across a wide spectrum of distributions. Its asymptotic relative efficiency with respect to the LS-based method is closely related to that of the signed-rank Wilcoxon test with respect to the t test. Moreover, the proposed variable selection method can identify the true model consistently, and the resulting estimator can be as efficient as the oracle estimator. Simulation studies show that our procedure has better performance than the LS-based method when the errors deviate from normality.
Starting Page	85
Ending Page	118
Page Count	34
File Format	PDF
ISSN	00261335
Journal	Metrika
Volume Number	78
Issue Number	1
e-ISSN	1435926X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-05-14
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	KLASSO Oracle property Polynomial spline Rank regression Robust estimation Robust model selection SCAD Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A new variable selection approach for varying coefficient models

Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression

Robust estimation for spatial semiparametric varying coefficient partially linear regression

Variable selection for varying coefficient models with measurement errors

Variable selection for generalized varying coefficient models with longitudinal data

Variable selection for single-index varying-coefficient model

Varying coefficient partially functional linear regression models

Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty

Scad-penalized regression in high-dimensional partially linear models (2009)

Robust spline-based variable selection in varying coefficient model

Similar Documents

A new variable selection approach for varying coefficient models

Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression

Robust estimation for spatial semiparametric varying coefficient partially linear regression

Variable selection for varying coefficient models with measurement errors

Variable selection for generalized varying coefficient models with longitudinal data

Variable selection for single-index varying-coefficient model

Varying coefficient partially functional linear regression models

Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty

Scad-penalized regression in high-dimensional partially linear models (2009)

Robust spline-based variable selection in varying coefficient model