Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model
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...
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| author | Mingquan Wang Xiaohua Ma Xinrui Wang Jun Wang Xiuqing Zhou Qibing Gao |
| author_facet | Mingquan Wang Xiaohua Ma Xinrui Wang Jun Wang Xiuqing Zhou Qibing Gao |
| author_sort | Mingquan Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0eb5a66ea4c14418bee861b55ce3341c |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-0eb5a66ea4c14418bee861b55ce3341c2025-01-24T13:39:41ZengMDPI AGMathematics2227-73902025-01-0113219210.3390/math13020192Smoothing Estimation of Parameters in Censored Quantile Linear Regression ModelMingquan Wang0Xiaohua Ma1Xinrui Wang2Jun Wang3Xiuqing Zhou4Qibing Gao5School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, ChinaSchool of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, ChinaCollege of International Languages and Cultures, Hohai University, Nanjing 211100, ChinaSchool of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, ChinaSchool of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, ChinaSchool of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, ChinaIn 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.https://www.mdpi.com/2227-7390/13/2/192censored quantile regressionhigh-dimensional datasmoothing estimation |
| spellingShingle | Mingquan Wang Xiaohua Ma Xinrui Wang Jun Wang Xiuqing Zhou Qibing Gao Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model Mathematics censored quantile regression high-dimensional data smoothing estimation |
| title | Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model |
| title_full | Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model |
| title_fullStr | Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model |
| title_full_unstemmed | Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model |
| title_short | Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model |
| title_sort | smoothing estimation of parameters in censored quantile linear regression model |
| topic | censored quantile regression high-dimensional data smoothing estimation |
| url | https://www.mdpi.com/2227-7390/13/2/192 |
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