Variable selection and weighted composite quantile estimation of regression parameters with left-truncated data

Mei Yao, Jiang-Feng Wang, Lu Lin and Yu-Xin Wang

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 18, 4469-4482

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.

Date: 2018
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DOI: 10.1080/03610926.2017.1376089

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