Showing 1 - 10 of 31
Persistent link: https://www.econbiz.de/10009770915
Data from the automatic monitoring of intensive care patients exhibits trends, outliers, and level changes as well as periods of relative constancy. All this is overlaid with a high level of noise and there are dependencies between the different items measured. Current monitoring systems tend to...
Persistent link: https://www.econbiz.de/10009775959
We discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends...
Persistent link: https://www.econbiz.de/10003835959
We describe a stochastic model based on a branching process for analyzing surveillance data of infectious diseases that allows to make forecasts of the future development of the epidemic. The model is based on a Poisson branching process with immigration with additional adjustment for possible...
Persistent link: https://www.econbiz.de/10002638731
Tests for shift detection in locally-stationary autoregressive time series are constructed which resist contamination by a substantial amount of outliers. Tests based on a comparison of local medians standardized by a highly robust estimate of the variability show reliable performance in a broad...
Persistent link: https://www.econbiz.de/10003835696
Persistent link: https://www.econbiz.de/10001813114
Robust versions of the exponential and Holt-Winters smoothing method for forecasting are presented. They are suitable for forecasting univariate time series in presence of outliers. The robust exponential and Holt-Winters smoothing methods are presented as a recursive updating scheme. Both the...
Persistent link: https://www.econbiz.de/10014220554
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...
Persistent link: https://www.econbiz.de/10009770914
Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which...
Persistent link: https://www.econbiz.de/10009779502
Persistent link: https://www.econbiz.de/10010347321