Composite quantile regression for heteroscedastic partially linear varying-coefficient models with missing censoring indicators-Reference-Cited by-同舟云学术

Composite quantile regression for heteroscedastic partially linear varying-coefficient models with missing censoring indicators

Published:2022-10-17 Issue:3 Volume:93 Page:341-365
ISSN:0094-9655
Container-title:Journal of Statistical Computation and Simulation
language:en
Short-container-title:Journal of Statistical Computation and Simulation

Author:

Zou Yuye¹²,Fan Guoliang¹,Zhang Riquan²³

Affiliation:

1. School of Economics and Management, Shanghai Maritime University, Shanghai, People's Republic of China

2. Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China

3. School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai, People's Republic of China

Funder

National Natural Science Foundation of China

National Statistical Science Research Project

National Social Science Foundation Key Program

111 Project of China

Natural Science Foundation of Shanghai

Project of Humanities and Social Science Foundation of Ministry of Education

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/00949655.2022.2108030

Reference26 articles.

1. Local Polynomial Fitting in Semivarying Coefficient Model

2. Efficient estimation of a semiparametric partially linear varying coefficient model

3. Profile likelihood inferences on semiparametric varying-coefficient partially linear models

4. Regression Quantiles

5. Semiparametric quantile regression estimation in dynamic models with partially varying coefficients