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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.
false
en
chapters
1984-01-01
1984
2019-04-15T15:23
256-272
Robust Regression by Means of S-Estimators
http://link.springer.com/10.1007/978-1-4615-7821-5_15
chapter
dimensions_id
pub.1043566420
doi
10.1007/978-1-4615-7821-5_15
P.
Rousseeuw
Springer US
New York, NY
Statistics
Yohai
V.
Jürgen
Franke
Douglas
Martin
Wolfgang
Härdle
CSOO (M 205), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
Vrije Universiteit Brussel
cf85bf3d4b4d52fb2c78c0539787aa52a4e0ec426bdd0fcab686919bc68cf277
readcube_id
Mathematical Sciences
Robust and Nonlinear Time Series Analysis
978-1-4615-7821-5
978-0-387-96102-6
Springer Nature - SN SciGraph project