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Robust versions of the exponential and Holt-Winters smoothing method for forecasting are presented. They are suitable for forecasting univariate time series in the presence of outliers. The robust exponential and Holt-Winters smoothing methods are presented as recursive updating schemes that apply the standard technique to pre-cleaned data. Both the update(More)
A better understanding of disease progression is beneficial for early diagnosis and appropriate individual therapy. There are many different approaches for statistical modelling of disease progression proposed in the literature, including simple path models up to complex restricted Bayesian networks. Important fields of application are diseases like cancer(More)
Abrupt shifts in the level of a time series represent important information and should be preserved in statistical signal extraction. We investigate rules for detecting level shifts that are resistant to outliers and which work with only a short time delay. The properties of robustified versions of the t-test for two independent samples and its(More)
Breathing rate (RR), end-tidal percent CO2, and EEG were obtained in three groups: psychiatric referral subjects presenting with anxiety, panic phobia, depression and migraine; a group of idiopathic seizure sufferers; and a group of asymptomatic controls. Virtually all the noncontrol subjects were found to show moderate to severe hyperventilation and the(More)
Nowadays physicians are confronted with high-dimensional data generated by clinical information systems. The proper extraction and interpretation of the information contained in such massive data sets, which are often observed with high sampling frequencies, can hardly be done by experience only. This yields new perspectives of data recording and also sets(More)
We discuss moving window techniques for fast extraction of a signal comprising monotonic trends and abrupt shifts from a noisy time series with irrelevant spikes. Running medians remove spikes and preserve shifts, but they deteriorate in trend periods. Modified trimmed mean filters use a robust scale estimate such as the median absolute deviation about the(More)
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 online monitoring, where the computational effort is often(More)
Standard median filters preserve abrupt shifts (edges) and remove impulsive noise (outliers) from a constant signal but they deteriorate in trend periods. Finite impulse response median hybrid (FMH) filters are more flexible and also preserve shifts, but they are much more vulnerable to outliers. Application of robust regression, in particular of the(More)