Heteroscedasticity identification and variable selection via multiple quantile regression-Reference-Cited by-同舟云学术

Heteroscedasticity identification and variable selection via multiple quantile regression

Published:2023-08-05 Issue:2 Volume:94 Page:297-314
ISSN:0094-9655
Container-title:Journal of Statistical Computation and Simulation
language:en
Short-container-title:Journal of Statistical Computation and Simulation

Author:

Wang Mingqiu¹,Kang Xiaoning²^ORCID,Liang Jiajuan³,Wang Kun¹,Wu Yuanshan⁴

Affiliation:

1. School of Statistics and Data Science, Qufu Normal University, Qufu, People's Republic of China

2. International Business College and Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian, People's Republic of China

3. Division of Science and Technology, BNU-HKBU United International College, Zhuhai, People's Republic of China

4. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei, People's Republic of China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province

Department of Education of Liaoning Province

United International College (UIC) Start-up Research Fund

Science Foundation of Ministry of Education of China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00949655.2023.2243533

Reference32 articles.

1. Use of Modified Profile Likelihood for Improved Tests of Constancy of Variance in Regression

2. Testing heteroscedasticity in nonparametric regression

3. Heteroscedasticity checks for regression models

4. Assessing the Adequacy of Variance Function in Heteroscedastic Regression Models

5. A NONPARAMETRIC GOODNESS-OF-FIT-BASED TEST FOR CONDITIONAL HETEROSKEDASTICITY