Univariate goodness-of-fit tests for randomly censored data: tests' adaptation versus data transformation

Abstract

Recently, several approaches for adaptation of goodness of fit tests for censored data have been proposed. This paved the way for the bunch of goodness of tests for such data. However, those tests usually depends on censoring distribution which is unknown in practice, and the application of resampling procedures is indispensable, but computationally expensive, step toward obtaining p-values. Here, we present an imputation procedure that can serve as an alternative approach to adaptation proposal. Additionally, we illustrate proposal on several characterization based exponentiality tests proposed so far.