Data Augmentation Based Quantile Regression Estimation for Censored Partially Linear Additive Model

Lu Li (), Ruiting Hao () and Xiaorong Yang ()
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Lu Li: Zhejiang Gongshang University
Ruiting Hao: Zhejiang Gongshang University
Xiaorong Yang: Zhejiang Gongshang University

Computational Economics, 2024, vol. 64, issue 2, No 15, 1083-1112

Abstract: Abstract As a common semiparametric mode, the partially linear additive model has flexible structures, and it has been widely used in practice. In this paper, we study the quantile regression estimation of the model when its responses are censored. In particular, we consider a more general censoring framework, which allows different types of censoring (left, right, double, or interval censoring) simultaneously. Based on the general principles of data augmentation, we propose a unified iterative algorithm, where the censored data is imputed by sampling data from the quantile process, and regression parameters are refitted by using bootstrap samples. Monte Carlo simulations are conducted to verify the finite-sample properties of the method, and the results show its good performance. The application to Boston housing price data further illustrates the advantages of this method in practice.

Keywords: Partially linear additive model; Censored responses; Data augmentation; Quantile regression; B-splines approximation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10473-5

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