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...

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...

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable...

Robustified rank tests, applying a robust scale estimator, are investigated for reliable and fast shift detection in time series. The tests show good power for sufficiently large shifts, low false detection rates for Gaussian noise and high robustness against outliers. Wilcoxon scores in...

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...

We discuss the robust estimation of a linear trend if the noise follows an autoregressive process of first order. We find the ordinary repeated median to perform well except for negative correlations. In this case it can be improved by a Prais-Winsten transformation using a robust...