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Original title:
How to down-weight observations in robust regression: A metalearning study
Authors: Kalina, Jan ; Pitra, Zbyněk
Document type: Papers
Conference/Event: MME 2018. International Conference Mathematical Methods in Economics /36./, Jindřichův Hradec (CZ), 20180912
Year: 2018
Language: eng
Abstract: Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination. We focus on comparing the prediction performance of the least weighted squares estimator with various weighting schemes. A broader spectrum of classification methods is applied and a support vector machine turns out to yield the best results. While results of a leave-1-out cross validation are very different from results of autovalidation, we realize that metalearning is highly unstable and its results should be interpreted with care. We also focus on discussing all possible limitations of the metalearning methodology in general.
Keywords: linear regression; metalearning; outliers; robust statistics
Project no.: GA17-07384S (CEP), GA17-01251S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: Mathematical Methods in Economics 2018. Conference Proceedings, ISBN 978-80-7378-371-6
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/11104/0287108
Permalink: http://www.nusl.cz/ntk/nusl-386572
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan ; Pitra, Zbyněk
Document type: Papers
Conference/Event: MME 2018. International Conference Mathematical Methods in Economics /36./, Jindřichův Hradec (CZ), 20180912
Year: 2018
Language: eng
Abstract: Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination. We focus on comparing the prediction performance of the least weighted squares estimator with various weighting schemes. A broader spectrum of classification methods is applied and a support vector machine turns out to yield the best results. While results of a leave-1-out cross validation are very different from results of autovalidation, we realize that metalearning is highly unstable and its results should be interpreted with care. We also focus on discussing all possible limitations of the metalearning methodology in general.
Keywords: linear regression; metalearning; outliers; robust statistics
Project no.: GA17-07384S (CEP), GA17-01251S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: Mathematical Methods in Economics 2018. Conference Proceedings, ISBN 978-80-7378-371-6
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/11104/0287108
Permalink: http://www.nusl.cz/ntk/nusl-386572
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2018-10-02, last modified 2023-12-06