Original title: Robust Regression Estimators: A Comparison of Prediction Performance
Authors: Kalina, Jan ; Peštová, Barbora
Document type: Papers
Conference/Event: MME 2017. International Conference Mathematical Methods in Economics /35./, Hradec Králové (CZ), 20170913
Year: 2017
Language: eng
Abstract: 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.
Keywords: linear regression; metalearning; outliers; prediction; robust estimation
Project no.: GA17-01251S (CEP)
Funding provider: GA ČR
Host item entry: MME 2017 Mathematical Methods in Economics, ISBN 978-80-7435-678-0

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available on demand via the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0271963

Permalink: http://www.nusl.cz/ntk/nusl-317286


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