Multivariate non-parametric tests of trend in the presence of missing data.

Abstract

When testing for trend one may be interested in either a monotone trend or a step trend. The former assumes that the population shifts monotonically over time without specifing when the shift occurrs. The latter assumes that the observations recorded before some specific time belong to a different population from the one recorded after that time. Our interest will be focused on tests for monotone trend. There exist parametric as well as nonparametric methods, univariate and multi-variate, to test for monotone trend. Practically, it occurs more often than not that some portion of the collected data are missing. There is at present a way to analyze incomplete data in the univariate case. In this work, we introduce nonparametric multivariate test statistics to test for monotone trend in the presence of missing data and deduce some corresponding asymptotic properties. (Abstract shortened by UMI.)