Abstract
There are at least two reasons why robust regression techniques are useful tools in robust time series analysis. First of all, one often wants to estimate autoregressive parameters in a robust way, and secondly, one sometimes has to fit a linear or nonlinear trend to a time series. In this paper we shall develop a class of methods for robust regression, and briefly comment on their use in time series. These new estimators are introduced because of their invulnerability to large fractions of contaminated data. We propose to call them “S-estimators” because they are based on estimators of scale.
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Rousseeuw, P., Yohai, V. (1984). Robust Regression by Means of S-Estimators. In: Franke, J., Härdle, W., Martin, D. (eds) Robust and Nonlinear Time Series Analysis. Lecture Notes in Statistics, vol 26. Springer, New York, NY. https://doi.org/10.1007/978-1-4615-7821-5_15
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DOI: https://doi.org/10.1007/978-1-4615-7821-5_15
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