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

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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.

Original languageEnglish (US)
Pages (from-to)183-213
Number of pages31
JournalLifetime Data Analysis
Volume26
Issue number1
DOIs
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • Applied Mathematics

Keywords

  • Functional delta method
  • Gaussian process
  • Lagrange multiplier
  • Nelson–Aalen estimator
  • Nonparametric maximum likelihood
  • Quantile function

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