A principal varying-coefficient model for quantile regression: Joint variable selection and dimension reduction-Reference-Cited by-同舟云学术

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A principal varying-coefficient model for quantile regression: Joint variable selection and dimension reduction

Published:2018-11 Issue: Volume:127 Page:269-280
ISSN:0167-9473
Container-title:Computational Statistics & Data Analysis
language:en
Short-container-title:Computational Statistics & Data Analysis

Author:

Zhao Weihua,Jiang Xuejun,Lian Heng

Funder

National Social Science Foundation of China

Natural Science Foundation of China

Natural Science Foundation of Guangdong Province of China

Shenzhen Sci-Tech Fund

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Statistics and Probability

Reference45 articles.

1. Estimating linear restrictions on regression coefficients for multivariate normal distributions;Anderson;Ann. Math. Statist.,1951

2. Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors;Breheny;Stat. Comput.,2015

3. Optimal selection of reduced rank estimators of high-dimensional matrices;Bunea;Ann. Statist.,2011

4. Joint variable and rank selection for parsimonious estimation of high-dimensional matrices;Bunea;Ann. Statist.,2012

5. Semiparametric quantile regression estimation in dynamic models with partially varying coefficients;Cai;J. Econometrics,2012

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