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Semiparametric quantile regression with random censoring

  • Francesco BravoEmail author
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Abstract

This paper considers estimation and inference in semiparametric quantile regression models when the response variable is subject to random censoring. The paper considers both the cases of independent and dependent censoring and proposes three iterative estimators based on inverse probability weighting, where the weights are estimated from the censoring distribution using the Kaplan–Meier, a fully parametric and the conditional Kaplan–Meier estimators. The paper proposes a computationally simple resampling technique that can be used to approximate the finite sample distribution of the parametric estimator. The paper also considers inference for both the parametric and nonparametric components of the quantile regression model. Monte Carlo simulations show that the proposed estimators and test statistics have good finite sample properties. Finally, the paper contains a real data application, which illustrates the usefulness of the proposed methods.

Keywords

Inverse probability of censoring Local linear estimation M-M algorithm 

Supplementary material

10463_2018_688_MOESM1_ESM.pdf (230 kb)
Supplementary material 1 (pdf 230 KB)

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Copyright information

© The Institute of Statistical Mathematics, Tokyo 2018

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of YorkYorkUK

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