Nonparametric k-sample tests with panel count data

We study the nonparametric k-sample test problem with panel count data. The asymptotic normality of a smooth functional of the nonparametric maximum pseudo-likelihood estimator (Wellner & Zhang, 2000) is established under some mild conditions. We construct a class of easy-to-implement nonparametric tests for comparing mean functions of k populations based on this asymptotic normality. We conduct various simulations to validate and compare the tests. The simulations show that the tests perform quite well and generally have good power to detect differences among the mean functions. The method is illustrated with a real-life example. Copyright 2006, Oxford University Press.