Noisy observations form the basis for almost every scientific research and especially in environmental monitoring. The Noise is often an effect of imprecise instruments which cause measurement errors. If the noise variance is known it is possible to filter out the contaminating noise from the...

In many fields of applied statistics samples from several locations in an investigation area are taken repeatedly over time. Especially in environmental monitoring the chemical and physical conditions in water, air and soil are measured using fixed and possibly mobile monitoring stations. The...

The paper considers the problem of non-parametric regression with emphasis on controlling the number of local extrema. Two methods, the run method and the taut string-wavelet method, are introduced and analysed on standard test beds. It is shown that the number and location of local extreme...

The secondary structure classification of amino acid sequences can be carried out by a statistical analysis of sequence and structure data using state-space models. Aiming at this classification, a modified filter algorithm programmed in S is applied to data of three proteins. The application...

Various aspects of the wavelet approach to nonparametric regression are considered, with the overall aim of extending the scope of wavelet techniques, to irregularlyspaced data, to regularly-spaced data sets of arbitrary size, to heteroscedastic and correlated data, and to data some of which may...

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...

For a time-continuous discrete-state Markov process as model for rating transitions, we study the time-stationarity by means of a likelihood ratio test. For multiple Markov process data from a multiplicative intensity model, maximum likelihood parameter estimates can be represented as martingale...

The repeated median line estimator is a highly robust method for fitting a regression line to a set of n data points in the plane. In this paper, we consider the problem of updating the estimate after a point is removed from or added to the data set. This problem occurs e.g. in statistical...