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How to down-weight observations in robust regression: A metalearning study
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SYSNO ASEP 0493805 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název How to down-weight observations in robust regression: A metalearning study Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Pitra, Zbyněk (UIVT-O) RID, ORCID, SAIZdroj.dok. Mathematical Methods in Economics 2018. Conference Proceedings. - Prague : MatfyzPress, 2018 / Váchová L. ; Kratochvíl V. - ISBN 978-80-7378-371-6 Rozsah stran s. 204-209 Poč.str. 6 s. Forma vydání Tištěná - P Akce MME 2018. International Conference Mathematical Methods in Economics /36./ Datum konání 12.09.2018 - 14.09.2018 Místo konání Jindřichův Hradec Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova metalearning ; robust statistics ; linear regression ; outliers Vědní obor RIV BB - Aplikovaná statistika, operační výzkum Obor OECD Statistics and probability CEP GA17-07384S GA ČR - Grantová agentura ČR GA17-01251S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000507455300036 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2019
Počet záznamů: 1