The k-sample problem with left-truncated and right-censored data

A. Lago, J. C. Pardo Fernández, J. de Uña Álvarez

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

Nonparametric Statistics: Nonparametric Test
June 13, 2025  11:00 AM
MR 1

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