A Robustified Metalearning Procedure for Regression Estimators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00510771" target="_blank" >RIV/67985556:_____/19:00510771 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/19:00510554
Result on the web
<a href="http://dx.doi.org/10.18267/pr.2019.los.186.61" target="_blank" >http://dx.doi.org/10.18267/pr.2019.los.186.61</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18267/pr.2019.los.186.61" target="_blank" >10.18267/pr.2019.los.186.61</a>
Alternative languages
Result language
angličtina
Original language name
A Robustified Metalearning Procedure for Regression Estimators
Original language description
Metalearning represents a useful methodology for selecting and recommending a suitable algorithm or method for a new dataset exploiting a database of training datasets. While metalearning is potentially beneficial for the analysis of economic data, we must be aware of its instability and sensitivity to outlying measurements (outliers) as well as measurement errors. The aim of this paper is to robustify the metalearning process. First, we prepare some useful theoretical tools exploiting the idea of implicit weighting, inspired by the least weighted squares estimator. These include a robust coefficient of determination, a robust version of mean square error, and a simple rule for outlier detection in linear regression. We perform a metalearning study for recommending the best linear regression estimator for a new dataset (not included in the training database). The prediction of the optimal estimator is learned over a set of 20 real datasets with economic motivation, while the least squares are compared with several (highly) robust estimators. We investigate the effect of variable selection on the metalearning results. If the training as well as validation data are considered after a proper robust variable selection, the metalearning performance is improved remarkably, especially if a robust prediction error is used.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10101 - Pure mathematics
Result continuities
Project
<a href="/en/project/GA17-07384S" target="_blank" >GA17-07384S: Nonparametric (statistical) methods in modern econometrics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 13th International Days of Statistics and Economics Conference Proceedings
ISBN
978-80-87990-18-6
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
617-626
Publisher name
Melandrium
Place of publication
Slaný
Event location
Prague
Event date
Sep 5, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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