A goodness of fit test for the survival function under random right censoring

Abstract

The present article contributes a goodness of fit test for the survival function under random right censoring. The test is based on a central limit theorem for the Integrated Square Error of an already existing in the literature kernel survival function estimate. Establishment of its asymptotic distribution yields the proposed test statistic for drawing decision on the null hypothesis of correctness of the assumed survival function. Numerical simulations quantify the empirical nominal level and power of the suggested test for various sample sizes and amounts of censoring and facilitate comparison with the power of the data driven Neyman goodness of fit test for censored samples.