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An estimating equation for censored and truncated quantile regression

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  • Frumento, Paolo
  • Bottai, Matteo

Abstract

An estimation equation for censored, truncated quantile regression is introduced. The asymptotic covariance matrix has a relatively simple expression and can be estimated from the data. Simulation results are presented, and the described estimator is used to evaluate the effects of birth weight on percentiles of survival time after age 65 with a population-based cohort of Swedish men. The proposed method is efficiently implemented in the R package ctqr.

Suggested Citation

  • Frumento, Paolo & Bottai, Matteo, 2017. "An estimating equation for censored and truncated quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 53-63.
    • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:53-63
    DOI: 10.1016/j.csda.2016.08.015
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    References listed on IDEAS

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    Cited by:

    1. Du, Jiang & Zhang, Zhongzhan & Xie, Tianfa, 2018. "A weighted M-estimator for linear regression models with randomly truncated data," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 90-94.
    2. Liang Yang & Zhengxiao Li & Shengwang Meng, 2020. "Risk Loadings in Classification Ratemaking," Papers 2002.01798, arXiv.org, revised Jan 2022.

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