Model Checks for Two-Sample Location-Scale

• Activity Number: 53 - New Developments in Survival Analysis
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Biometrics Section
• Author(s): Atefeh Javidialsaadi* and Shoubhik Mondal and Sundarraman Subramanian
• Companies: New Jersey Institute of technology and Boehringer Ingelheim and New Jersey Institute of Technology
• Keywords: Survival function; Kaplan Meier estimator; Nonparametri maximum likelihood ; Gaussian process; Log likelihood ratio statistics; Adaptive rule

Abstract

Two-sample location-scale refers to a model that permits a pair of standardized random variables to have a common base or standard distribution. The normal distribution with mean 0 and variance 1 is an example of a standard distribution. Function-based hypothesis testing in these models refers to formal tests that would help decide whether or not two samples may have come from some location-scale family of distributions. For uncensored data, an approach of testing based on plug-in empirical likelihood (PEL) is carried out with sample means and standard deviations as the plug-ins. The method readily extends to censored data, where censoring adjusted moment estimators help provide the requisite plug-ins. The large sample null distribution of the PEL statistic is derived. Since it is not distribution free, a two-sample location-scale appropriate resampling is employed to obtain thresholds needed for the testing. Numerical studies are carried out to investigate the performance of the proposed method. Real examples are presented for both the uncensored and censored cases.