We analyze multivariate binary time series using a mixed parameterization in terms of the conditional expectations given the past and the pairwise canonical interactions among contemporaneous variables. This allows consistent inference on the influence of past variables even if the...

Im Rahmen des Projektes ?Zeitreihenanalytische Methoden zur Behandlung von Online-Monitoring-Daten aus der Intensivmedizin? im Sonderforschungsbereich 475 wird eine klinische Studie zur Evaluierung und zum Vergleich von Alarm-Algorithmen für die Patientenüberwachung auf Intensivstationen...

We develop and test a robust procedure for extracting an underlying signal in form of a time-varying trend from very noisy time series. The application we have in mind is online monitoring data measured in intensive care, where we find periods of relative constancy, slow monotonic trends, level...

We examine the hypothesis of an increase of humus disintegration by analyzing chemical substances measured in the seepage water of a German forest. Problems arise because of a large percentage of missing observations. We use a regression model with spatial and temporal effects constructed in an...

We discuss robust estimation of INARCH models for count time series, where each observation conditionally on its past follows a negative binomial distribution with a constant scale parameter, and the conditional mean depends linearly on previous observations. We develop several robust...

In many situations, it is crucial to estimate the variance properly. Ordinary variance estimators perform poorly in the presence of shifts in the mean. We investigate an approach based on non-overlapping blocks, which yields good results in change-point scenarios. We show the strong consistency...

We study the problem of intervention effects generating various types of outliers in a linear count time series model. This model belongs to the class of observation driven models and extends the class of Gaussian linear time series models within the exponential family framework. Studies about...

In critical care extremely high dimensional time series are generated by clinical information systems. This yields new perspectives of data recording and also causes a new challenge for statistical methodology. Recently graphical correlation models have been developed for analysing the partial...