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Regression-based, regression-free and model-free approaches for robust online scale estimation
- Source :
- Journal of Statistical Computation and Simulation, 80(9), 1023-1040. Taylor & Francis Ltd
- Publication Year :
- 2010
- Publisher :
- Taylor & Francis Ltd, 2010.
-
Abstract
- This paper compares the 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 regression-free 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.
- Subjects :
- Statistics and Probability
Series (mathematics)
Scale (ratio)
Computer science
Applied Mathematics
Univariate
Estimator
real-time estimation
robustness
Model free
Regression
Robustness (computer science)
Modeling and Simulation
Statistics
Scale estimation
Statistics, Probability and Uncertainty
Volatility (finance)
time series
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 15635163 and 00949655
- Volume :
- 80
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Journal of Statistical Computation and Simulation
- Accession number :
- edsair.doi.dedup.....bec102e2e45277d07f3480f53fde05cd