If we are given a time series of economic data, a basic question is whether the series is stationary or a random walk, i.e., has a unit root. Whereas the problem to test the unit root null hypothesis against the alternative of stationarity is well studied in the context of classic hypothesis...

Dickey-Fuller control charts aim at monitoring a random walk until a given time horizon to detect stationarity as early as possible. That problem appears in many fields, especially in econometrics and the analysis of economic equilibria. To improve upon asymptotic control limits (critical...

An important problem of the statistical analysis of time series is to detect change-points in the mean structure. Since this problem is a one-dimensional version of the higher dimensional problem of detecting edges in images, we study detection rules which benefit from results obtained in image...

An attractive nonparametric method to detect change-points sequentially is to apply control charts based on kernel smoothers. Recently, the strong convergence of the associated normed delay associated with such a sequential stopping rule has been studied under sequences of out-of-control models....

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...

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...