Počet záznamů: 1
On Robust Estimation of Error Variance in (Highly) Robust Regression
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SYSNO ASEP 0522581 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název On Robust Estimation of Error Variance in (Highly) Robust Regression Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Tichavský, Jan (UIVT-O)Zdroj.dok. Measurement Science Review. - : Sciendo - ISSN 1335-8871
Roč. 20, č. 1 (2020), s. 6-14Poč.str. 9 s. Jazyk dok. eng - angličtina Země vyd. SK - Slovensko Klíč. slova high robustness ; robust regression ; outliers ; variance of errors ; least weighted squares ; simulation Vědní obor RIV BB - Aplikovaná statistika, operační výzkum Obor OECD Statistics and probability CEP GA19-05704S GA ČR - Grantová agentura ČR Způsob publikování Open access Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000517823000002 EID SCOPUS 85081789945 DOI 10.2478/msr-2020-0002 Anotace The linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on estimating the variance of the random regression errors. We particularly focus on the least weighted squares estimator, for which we review its properties and´propose new weighting schemes together with corresponding estimates for the variance of disturbances. An illustrative example revealing the idea of the estimator to down-weight individual measurements is presented. Further, two numerical simulations presented here allow to compare various estimators. They verify the theoretical results for the least weighted squares to be meaningful. MM-estimators turn out to yield the best results in the simulations in terms of both accuracy and precision. The least weighted squares (with suitable weights) remain only slightly behind in terms of the mean square error and are able to outperform the much more popular least trimmed squares estimator, especially for smaller sample sizes Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2021 Elektronická adresa http://hdl.handle.net/11104/0307056
Počet záznamů: 1