A class of nonparametric bivariate survival function estimators for randomly censored and truncated data

Hongsheng Dai (Lead / Corresponding author), Marialuisa Restaino, Huan Wang

    Research output: Contribution to journalArticle

    2 Citations (Scopus)
    69 Downloads (Pure)

    Abstract

    This paper proposes a class of nonparametric estimators for the bivariate survival function estimation under both random truncation and random censoring. In practice, the pair of random variables under consideration may have certain parametric relationship. The proposed class of nonparametric estimators uses such parametric information via a data transformation approach and thus provides more accurate estimates than existing methods without using such information. The large sample properties of the new class of estimators and a general guidance of how to find a good data transformation are given. The proposed method is also justified via a simulation study and an application on an economic data set.

    Original languageEnglish
    Pages (from-to)736-751
    Number of pages16
    JournalJournal of Nonparametric Statistics
    Volume28
    Issue number4
    Early online date30 Aug 2016
    DOIs
    Publication statusPublished - Oct 2016

    Fingerprint

    Truncated Data
    Survival Function
    Censored Data
    Data Transformation
    Nonparametric Estimator
    Estimator
    Random Censoring
    Function Estimation
    Truncation
    Guidance
    Random variable
    Simulation Study
    Economics
    Estimate
    Class
    Data transformation

    Keywords

    • Bivariate survival function
    • correlated failure times
    • data transformation method
    • random censoring
    • random truncation

    Cite this

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    A class of nonparametric bivariate survival function estimators for randomly censored and truncated data. / Dai, Hongsheng (Lead / Corresponding author); Restaino, Marialuisa; Wang, Huan.

    In: Journal of Nonparametric Statistics, Vol. 28, No. 4, 10.2016, p. 736-751.

    Research output: Contribution to journalArticle

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