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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In this paper, we propose a smoothing estimation method for censored quantile regression models. The method associates the convolutional smoothing estimation with the loss function, which is quadratically derivable and globally convex by using a non-negative kernel function. Thus, the parameters of the regression model can be computed by using the gradient-based iterative algorithm. We demonstrate the convergence speed and asymptotic properties of the smoothing estimation for large samples in high dimensions. Numerical simulations show that the smoothing estimation method for censored quantile regression models improves the estimation accuracy, computational speed, and robustness over the classical parameter estimation method. The simulation results also show that the parametric methods perform better than the KM method in estimating the distribution function of the censored variables. Even if there is an error setting in the distribution estimation, the smoothing estimation does not fluctuate too much.

Details

Title
Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model
Author
Wang, Mingquan 1   VIAFID ORCID Logo  ; Ma, Xiaohua 1 ; Wang, Xinrui 2 ; Wang, Jun 1 ; Zhou, Xiuqing 1   VIAFID ORCID Logo  ; Gao, Qibing 1 

 School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China; [email protected] (M.W.); [email protected] (X.M.); [email protected] (J.W.) 
 College of International Languages and Cultures, Hohai University, Nanjing 211100, China; [email protected] 
First page
192
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
22277390
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3159530016
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.