Non parametric hazard estimation with dependent censoring using penalized likelihood and an assumed copula

Jing Xu*, Jun Ma, Tania Prvan

*Corresponding author for this work

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    This article introduces a novel non parametric penalized likelihood hazard estimation when the censoring time is dependent on the failure time for each subject under observation. More specifically, we model this dependence using a copula, and the method of maximum penalized likelihood (MPL) is adopted to estimate the hazard function. We do not consider covariates in this article. The non negatively constrained MPL hazard estimation is obtained using a multiplicative iterative algorithm. The consistency results and the asymptotic properties of the proposed hazard estimator are derived. The simulation studies show that our MPL estimator under dependent censoring with an assumed copula model provides a better accuracy than the MPL estimator under independent censoring if the sign of dependence is correctly specified in the copula function. The proposed method is applied to a real dataset, with a sensitivity analysis performed over various values of correlation between failure and censoring times.

    Original languageEnglish
    Pages (from-to)11383-11403
    Number of pages21
    JournalCommunications in Statistics - Theory and Methods
    Volume46
    Issue number22
    DOIs
    Publication statusPublished - 2017

    Keywords

    • copula
    • dependent censoring
    • maximum penalized likelihood
    • multiplicative iterative algorithm
    • non parametric hazard estimation.

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