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Median regression from twice censored data

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  • Subramanian, Sundarraman

Abstract

An adjusted least absolute deviation estimating function, founded on the inverse (probability of) censoring weighted approach, is proposed. Covariate-free left and right censoring is assumed. When left censoring is absent, the proposed estimating function reduces to its right-censored counterpart. Consistency and asymptotic normality of the estimator of the regression parameter are derived. Finite sample performance is investigated via simulations. Application of the proposed method is illustrated using some synthetic data sets.

Suggested Citation

  • Subramanian, Sundarraman, 2021. "Median regression from twice censored data," Statistics & Probability Letters, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:stapro:v:168:y:2021:i:c:s0167715220302583
    DOI: 10.1016/j.spl.2020.108955
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    References listed on IDEAS

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    4. Lindgren, Anna, 1997. "Quantile regression with censored data using generalized L1 minimization," Computational Statistics & Data Analysis, Elsevier, vol. 23(4), pages 509-524, February.
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    6. Lin, Guixian & He, Xuming & Portnoy, Stephen, 2012. "Quantile regression with doubly censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 797-812.
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    Cited by:

    1. Subramanian, Sundarraman, 2022. "Simultaneous confidence bands for survival functions from twice censorship," Statistics & Probability Letters, Elsevier, vol. 186(C).

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