Název: Robust Regression Estimators: A Comparison of Prediction Performance
Autoři: Kalina, Jan ; Peštová, Barbora
Typ dokumentu: Příspěvky z konference
Konference/Akce: MME 2017. International Conference Mathematical Methods in Economics /35./, Hradec Králové (CZ), 20170913
Rok: 2017
Jazyk: eng
Abstrakt: Regression represents an important methodology for solving numerous tasks of applied econometrics. This paper is devoted to robust estimators of parameters of a linear regression model, which are preferable whenever the data contain or are believed to contain outlying measurements (outliers). While various robust regression estimators are nowadays available in standard statistical packages, the question remains how to choose the most suitable regression method for a particular data set. This paper aims at comparing various regression methods on various data sets. First, the prediction performance of common robust regression estimators are compared on a set of 24 real data sets from public repositories. Further, the results are used as input for a metalearning study over 9 selected features of individual data sets. On the whole, the least trimmed squares turns out to be superior to the least squares or M-estimators in the majority of the data sets, while the process of metalearning does not succeed in a reliable prediction of the most suitable estimator for a given data set.
Klíčová slova: linear regression; metalearning; outliers; prediction; robust estimation
Číslo projektu: GA17-01251S (CEP)
Poskytovatel projektu: GA ČR
Zdrojový dokument: MME 2017 Mathematical Methods in Economics, ISBN 978-80-7435-678-0

Instituce: Ústav informatiky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný v příslušném ústavu Akademie věd ČR.
Původní záznam: http://hdl.handle.net/11104/0271963

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-317286


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