| The Horvitz-Thompson Weighting Method for Quantile Regression Estimation in the Presence of Missing Covariates |
| Received:March 17, 2020 Revised:January 28, 2021 |
| Key Words:
Robust quantile regression missing covariates selection probability Kernel estimator weighting method
|
| Fund Project:Supported by the National Natural Science Foundation of China (Grant No.11861042) and the China Statistical Research Project (Grant No.2020LZ25). |
| Author Name | Affiliation | | Zhaoji CHU | Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, P. R. China | | Lingnan TAI | Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, P. R. China | | Wei XIONG | Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, P. R. China
School of Statistics, University of International Business and Economics, Beijing 100029, P. R. China | | Xu GUO | Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, P. R. China
School of Statistics, Beijing Normal University, Beijing 100875, P. R. China | | Maozai TIAN | Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, P. R. China
Department of Medical Engineering and Technology, Xinjiang Medical University, Xinjiang 830011, P. R. China
School of Statistics and Information, Xinjiang University of Finance and Economics, Xinjiang 830012, P. R. China |
|
| Hits: 356 |
| Download times: 236 |
| Abstract: |
| The lack of covariate data is one of the hotspots of modern statistical analysis. It often appears in surveys or interviews, and becomes more complex in the presence of heavy tailed, skewed, and heteroscedastic data. In this sense, a robust quantile regression method is more concerned. This paper presents an inverse weighted quantile regression method to explore the relationship between response and covariates. This method has several advantages over the naive estimator. On the one hand, it uses all available data and the missing covariates are allowed to be heavily correlated with the response; on the other hand, the estimator is uniform and asymptotically normal at all quantile levels. The effectiveness of this method is verified by simulation. Finally, in order to illustrate the effectiveness of this method, we extend it to the more general case, multivariate case and nonparametric case. |
| Citation: |
| DOI:10.3770/j.issn:2095-2651.2021.03.008 |
| View Full Text View/Add Comment Download reader |
|
|
|
