Motivated in part by applications in model selection in statistical genetics and sequential monitoring of financial data, we study an empirical process framework for a class of stopping rules which rely on kernel-weighted averages of past data. We are interested in the asymptotic distribution...

This paper studies nonparametric control charts to sequentially monitor dependent stochastic processes in continuous time with arbitrary but smooth drift functions m(t) to detect fast changes of m(t). Such methods are of particular interest when monitoring financial time series in order to...