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Quantile regression for interval censored data

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  • Xiuqing Zhou
  • Yanqin Feng
  • Xiuli Du

Abstract

As direct generalization of the quantile regression for complete observed data, an estimation method for quantile regression models with interval censored data is proposed, and the property of consistency is obtained. The property of asymptotic normality is also established with a bias converging to zero, and to reduce the bias, two bias correction methods are proposed. Methods proposed in this paper do not require the censoring vectors to be identically distributed, and can be applied to models with various covariates. Simulation results show that the proposed methods work well.

Suggested Citation

  • Xiuqing Zhou & Yanqin Feng & Xiuli Du, 2017. "Quantile regression for interval censored data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(8), pages 3848-3863, April.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:8:p:3848-3863
    DOI: 10.1080/03610926.2015.1073317
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    Cited by:

    1. Gustavo Javier Canavire-Bacarreza & Fernando Rios-Avila, 2022. "Recovering income distribution in the presence of interval-censored data," 2022 Stata Conference 19, Stata Users Group.
    2. ChunJing Li & Yun Li & Xue Ding & XiaoGang Dong, 2020. "DGQR estimation for interval censored quantile regression with varying-coefficient models," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-17, November.

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