Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2017

Goodness-of-fit test for randomly censored data based on maximum correlation

  • Identification data

    Identifier: RP:2458
    Handle: https://hdl.handle.net/20.500.11797/RP2458
    Authors:
    Grané, AureaStrzalkowska-Kominiak, Ewa
    Abstract:
    In this paper we study a goodness-of-fit test based on the maximum correlation coefficient, in the context of randomly censored data. We construct a new test statistic under general right- censoring and prove its asymptotic properties. Additionally, we study a special case, when the censoring mechanism follows the well-known Koziol-Green model. We present an extensive simulation study on the empirical power of these two versions of the test statistic, showing their ad- vantages over the widely used Pearson-type test. Finally, we apply our test to the head-and-neck cancer data.

  • Others:

    URV's Author/s: Grané, Aurea Strzalkowska-Kominiak, Ewa
    Keywords: Goodness-of-fit, Kaplan-Meier estimator, maximum correlation, random censoring
    Abstract: In this paper we study a goodness-of-fit test based on the maximum correlation coefficient, in the context of randomly censored data. We construct a new test statistic under general right- censoring and prove its asymptotic properties. Additionally, we study a special case, when the censoring mechanism follows the well-known Koziol-Green model. We present an extensive simulation study on the empirical power of these two versions of the test statistic, showing their ad- vantages over the widely used Pearson-type test. Finally, we apply our test to the head-and-neck cancer data.
    Journal publication year: 2017
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Goodness-of-fit, Kaplan-Meier estimator, maximum correlation, random censoring

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