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

Sundarraman Subramanian ()
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Sundarraman Subramanian: New Jersey Institute of Technology

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 1, No 9, 183-213

Abstract: 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.

Keywords: Functional delta method; Gaussian process; Lagrange multiplier; Nelson–Aalen estimator; Quantile function; Nonparametric maximum likelihood (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-018-09456-8

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