Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model

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Detalles Bibliográficos
Publicado en:	Mathematics vol. 13, no. 2 (2025), p. 192
Autor principal:	Wang, Mingquan
Otros Autores:	Ma, Xiaohua, Wang, Xinrui, Wang, Jun, Zhou, Xiuqing, Gao, Qibing
Publicado:	MDPI AG
Materias:	Iterative algorithms Accuracy Smoothing Asymptotic methods Regression analysis Parameter estimation Iterative methods Numerical models Regression models Adultery Distribution functions Quantiles Parameter robustness Asymptotic properties Methods Kernel functions
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	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.
ISSN:	2227-7390
DOI:	10.3390/math13020192
Fuente:	Engineering Database