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...

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...

In many applications one is interested to detect certain (known) patterns in the mean of a process with smallest delay. Using an asymptotic framework which allows to capture that feature, we study a class of appropriate sequential nonparametric kernel procedures under local nonparametric...