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A comparison of robust nonlinear regression methods by statistical learning
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SYSNO ASEP 0491783 Druh ASEP A - Abstrakt Zařazení RIV Záznam nebyl označen do RIV Zařazení RIV Není vybrán druh dokumentu Název A comparison of robust nonlinear regression methods by statistical learning Tvůrce(i) Kalina, Jan (UTIA-B)
Peštová, Barbora (UIVT-O) RID, SAIZdroj.dok. ISNPS 2018. Book of Abstracts. - Salerno, 2018 / La Rocca M. ; Liseo B. ; Parella M. L. ; Salmaso L. ; Tardella L. - ISBN 978-88-61970-00-7
S. 42-42Poč.str. 1 s. Akce ISNPS 2018. Conference of the International Society for Nonparametric Statistics /4./ Datum konání 11.06.2018 - 15.06.2018 Místo konání Salerno Země IT - Itálie Typ akce WRD Jazyk dok. eng - angličtina Země vyd. IT - Itálie Klíč. slova metalearning ; robust estimation ; nonlinear regression ; nonlinear regression quantiles ; heteroscedasticity Vědní obor RIV BB - Aplikovaná statistika, operační výzkum Obor OECD Statistics and probability Institucionální podpora UIVT-O - RVO:67985807 ; UTIA-B - RVO:67985556 Překlad anotace Various estimators for the standard nonlinear regression model are compared with a focus on methods which are robust to outlying measurements in the data. The main contribution is a metalearning study which has the aim to predict the most suitable estimator for a particular data set. Here, various versions of the nonlinear least weighted squares estimator are compared with nonlinear least squares, nonlinear least trimmed squares and a nonlinear regression median, where the last is a special case of nonlinear regression quantiles. The metalearning study is performed over a data base of 24 economic data sets. The nonlinear least weighted squares estimator is able to yield the best result for the most data sets. The metalearning study gives advice how to select appropriate weights for the nonlinear least weighted squares, particularly it reveals tests of normality and heteroscedasticity to play a crucial role in finding suitable weights. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2019
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