How to down-weight observations in robust regression: A metalearning study

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00493805" target="_blank" >RIV/67985807:_____/18:00493805 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/18:00506986
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
How to down-weight observations in robust regression: A metalearning study
Original language description
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.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Mathematical Methods in Economics 2018. Conference Proceedings
ISBN
978-80-7378-371-6
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
204-209
Publisher name
MatfyzPress
Place of publication
Prague
Event location
Jindřichův Hradec
Event date
Sep 12, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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