Multi-purpose open-end monitoring procedures for multivariate observations based on the empirical distribution function

We propose nonparametric open-end sequential testing procedures that can detect all types of changes in the contemporary distribution function of multivariate observations. Their asymptotic properties are theoretically investigated under stationarity and under alternatives to stationarity. Monte Carlo experiments reveal their good finite-sample behavior in the case of continuous univariate observations. A short data example concludes the work.