Constancy of distributions: nonparametric monitoring of probability distributions over time

In this paper we study stochastic processes which enable monitoring the possible changes of probability distributions over time. These processes may in particular be used to test the null hypothesis of no change. The monitoring processes are bivariate functions, of time and position at the measurement scale, and are approximated with zero mean Gaussian processes under the constancy hypothesis. One may then form Kolmogorov--Smirnov or other type of tests as functionals of the processes. To study null distributions of the resulting tests, we employ KMT-type inequalities to derive Cram\'er-type deviation results for (bootstrapped versions of) such tests statistics.