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A weighted quantile regression for nonlinear models with randomly censored data

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  • Hailin Feng
  • Qianqian Luo

Abstract

Quantile regression has great flexibility in describing the effect of covariate on response. In this paper, based on the censored median regression proposed by Zhou, we develop a new weighted quantile regression of nonlinear models when the response is randomly censored. Our method can be applied to more complicated quantile regression models at any quantile within (0, 1), which contains censored median regression. Furthermore, the consistency and asymptotic normality of the proposed estimator are derived under appropriate assumptions. Finally, the finite sample performance of the proposed estimator is examined by numerical simulation.

Suggested Citation

  • Hailin Feng & Qianqian Luo, 2021. "A weighted quantile regression for nonlinear models with randomly censored data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(18), pages 4167-4179, August.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:18:p:4167-4179
    DOI: 10.1080/03610926.2020.1713364
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