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Publication
Regression-based, regression-free and model-free approaches for robust online scale estimation
Journal Contribution - Journal Article
This paper compares methods for variability extraction from a univariate
time series in real time. The online scale estimation is achieved by applying
a robust scale functional to a moving time window. Scale estimators based
on the residuals of a preceding regression step are compared with regressionfree
and model-free techniques in a simulation study and in an application
to a real time series.
In the presence of level shifts or strong non-linear trends in the signal
level, the model-free scale estimators perform especially well. However, the
investigated regression-free and regression-based methods have higher breakdown
points, they are applicable to data containing temporal correlations,
and they are much more efficient.
time series in real time. The online scale estimation is achieved by applying
a robust scale functional to a moving time window. Scale estimators based
on the residuals of a preceding regression step are compared with regressionfree
and model-free techniques in a simulation study and in an application
to a real time series.
In the presence of level shifts or strong non-linear trends in the signal
level, the model-free scale estimators perform especially well. However, the
investigated regression-free and regression-based methods have higher breakdown
points, they are applicable to data containing temporal correlations,
and they are much more efficient.
Journal: Journal of Statistical Computation & Simulation
ISSN: 0094-9655
Volume: 80
Pages: 1023-1040
Publication year:2010
Keywords:real-time estimation, robustness, time series