Weighted quantile regression and testing for varying-coefficient models with randomly truncated data

Hong-Xia Xu, Guo-Liang Fan, Zhen-Long Chen () and Jiang-Feng Wang
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Hong-Xia Xu: Zhejiang Gongshang University
Guo-Liang Fan: Renmin University of China
Zhen-Long Chen: Zhejiang Gongshang University
Jiang-Feng Wang: Zhejiang Gongshang University

AStA Advances in Statistical Analysis, 2018, vol. 102, issue 4, No 4, 565-588

Abstract: Abstract This paper develops a varying-coefficient approach to the estimation and testing of regression quantiles under randomly truncated data. In order to handle the truncated data, the random weights are introduced and the weighted quantile regression (WQR) estimators for nonparametric functions are proposed. To achieve nice efficiency properties, we further develop a weighted composite quantile regression (WCQR) estimation method for nonparametric functions in varying-coefficient models. The asymptotic properties both for the proposed WQR and WCQR estimators are established. In addition, we propose a novel bootstrap-based test procedure to test whether the nonparametric functions in varying-coefficient quantile models can be specified by some function forms. The performance of the proposed estimators and test procedure are investigated through simulation studies and a real data example.

Keywords: Varying-coefficient models; Composite quantile regression; Randomly truncated data; Asymptotic normality; Bootstrap; 62G08; 62G20 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10182-018-0319-6

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