Linear Nonparametric Tests for Comparison of Counting Processes, with Applications to Censored Survival Data, Correspondent Paper

Abstract

This paper surveys linear nonparametric oneand k-sample tests for counting processes. The necessary probabilistic background is outlined and a master theorem proved, which may be specialized to most known asymptotic results for linear rank tests for censored data as well as to asymptotic results for oneand k-sample tests in more general situations, an important feature being that very general censoring patterns are allowed. A survey is given of existing tests and their relation to the general theory, and we mention examples of applications to Markov processes. We also discuss the relation of the present approach to classical nonparametric hypothesis testing theory based on permutation distributions.