The k-sample problem with left-truncated and right-censored data
In this talk, k-sample versions of the Kolmogorov-Smirnov and Cramér-von Mises tests are proposed for data subject to left truncation and right censoring. Their asymptotic behaviour under the null and alternative hypotheses is studied and a bootstrap resampling plan is proposed to approximate the null distribution of the new tests. The performance of such a method is studied via Monte Carlo. The power of the tests with finite sample sizes is addressed in a simulation study, where the classical log-rank test is also considered. The relative performance of the three tests will be discussed. An illustration with a real dataset regarding unemployment times will also be commented.
Keywords: Censoring Cramér-von Mises k-sample problem Kolmogorov-Smirnov truncation