Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model

Mingquan Wang, Xiaohua Ma, Xinrui Wang, Jun Wang, Xiuqing Zhou () and Qibing Gao ()
Additional contact information
Mingquan Wang: School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China
Xiaohua Ma: School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China
Xinrui Wang: College of International Languages and Cultures, Hohai University, Nanjing 211100, China
Jun Wang: School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China
Xiuqing Zhou: School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China
Qibing Gao: School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China

Mathematics, 2025, vol. 13, issue 2, 1-28

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.

Keywords: censored quantile regression; high-dimensional data; smoothing estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/2/192/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/2/192/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:2:p:192-:d:1562846

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().