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Implicitly weighted robust estimation of quantiles in linear regression
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SYSNO ASEP 0509648 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Implicitly weighted robust estimation of quantiles in linear regression Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Vidnerová, Petra (UIVT-O) RID, SAI, ORCIDZdroj.dok. Conference Proceedings. 37th International Conference on Mathematical Methods in Economics 2019. - České Budějovice : University of South Bohemia in České Budějovice, Faculty of Economics, 2019 / Houda M. ; Remeš R. - ISBN 978-80-7394-760-6 Rozsah stran s. 25-30 Poč.str. 6 s. Forma vydání Online - E Akce MME 2019: International Conference on Mathematical Methods in Economics /37./ Datum konání 11.09.2019 - 13.09.2019 Místo konání České Budějovice Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova regression quantiles ; robust regression ; outliers ; leverage points Vědní obor RIV BB - Aplikovaná statistika, operační výzkum Obor OECD Statistics and probability CEP GA19-05704S GA ČR - Grantová agentura ČR GA18-23827S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000507570400003 Anotace Estimation of quantiles represents a very important task in econometric regression modeling, while the standard regression quantiles machinery is well developed as well as popular with a large number of econometric applications. Although regression quantiles are commonly known as robust tools, they are vulnerable to the presence of leverage points in the data. We propose here a novel approach for the linear regression based on a specific version of the least weighted squares estimator, together with an additional estimator based only on observations between two different novel quantiles. The new methods are conceptually simple and comprehensible. Without the ambition to derive theoretical properties of the novel methods, numerical computations reveal them to perform comparably to standard regression quantiles, if the data are not contaminated by outliers. Moreover, the new methods seem much more robust on a simulated dataset with severe leverage points. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020
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