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Variable selection and weighted composite quantile estimation of regression parameters with left-truncated data |
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| Authors: | Mei Yao Lu Lin Yu-Xin Wang |
| Institution: | 1. School of Mathematics, Hefei University of Technology, Hefei, China;2. Shandong University Qilu Securities Institute for Financial Studies, Shandong University, Jinan, China;3. Shandong University Qilu Securities Institute for Financial Studies, Shandong University, Jinan, China;4. School of Economics, Hefei University of Technology, Hefei, China |
| Abstract: | In this paper, we consider the weighted composite quantile regression for linear model with left-truncated data. The adaptive penalized procedure for variable selection is proposed. The asymptotic normality and oracle property of the resulting estimators are also established. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods. |
| Keywords: | Asymptotic normality composite quantile regression left-truncated data oracle property variable selection |