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Function-based hypothesis testing in censored two-sample location-scale models

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  • Sundarraman Subramanian

    (New Jersey Institute of Technology)

Abstract

Function-based hypothesis testing in two-sample location-scale models has been addressed for uncensored data using the empirical characteristic function. A test of adequacy in censored two-sample location-scale models is lacking, however. A plug-in empirical likelihood approach is used to introduce a test statistic, which, asymptotically, is not distribution free. Hence for practical situations bootstrap is necessary for performing the test. A multiplier bootstrap and a model appropriate resampling procedure are given to approximate critical values from the null asymptotic distribution. Although minimum distance estimators of the location and scale are deployed for the plug-in, any consistent estimators can be used. Numerical studies are carried out that validate the proposed testing method, and real example illustrations are given.

Suggested Citation

  • Sundarraman Subramanian, 2020. "Function-based hypothesis testing in censored two-sample location-scale models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 183-213, January.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:1:d:10.1007_s10985-018-09456-8
    DOI: 10.1007/s10985-018-09456-8
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    References listed on IDEAS

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