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Least Weighted Absolute Value Estimator with an Application to Investment Data
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SYSNO ASEP 0535711 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Least Weighted Absolute Value Estimator with an Application to Investment Data Tvůrce(i) Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Kalina, Jan (UIVT-O) RID, SAI, ORCIDZdroj.dok. The 14th International Days of Statistics and Economics Conference Proceedings. - Slaný : Melandrium, 2020 / Löster T. ; Pavelka T. - ISBN 978-80-87990-22-3 Rozsah stran s. 1357-1366 Poč.str. 10 s. Forma vydání Tištěná - P Akce International Days of Statistics and Economics /14./ Datum konání 10.09.2020 - 12.09.2020 Místo konání Prague Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova robust regression ; regression median ; implicit weighting ; computational aspects ; nonparametric bootstrap Vědní obor RIV BB - Aplikovaná statistika, operační výzkum Obor OECD Statistics and probability CEP GA18-23827S GA ČR - Grantová agentura ČR GA19-05704S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 Anotace While linear regression represents the most fundamental model in current econometrics, the least squares (LS) estimator of its parameters is notoriously known to be vulnerable to the presence of outlying measurements (outliers) in the data. The class of M-estimators, thoroughly investigated since the groundbreaking work by Huber in 1960s, belongs to the classical robust estimation methodology (Jurečková et al., 2019). M-estimators are nevertheless not robust with respect to leverage points, which are defined as values outlying on the horizontal axis (i.e. outlying in one or more regressors). The least trimmed squares estimator seems therefore a more suitable highly robust method, i.e. with a high breakdown point (Rousseeuw & Leroy, 1987). Its version with weights implicitly assigned to individual observations, denoted as the least weighted squares estimator, was proposed and investigated in Víšek (2011). A trimmed estimator based on the 𝐿1-norm is available as the least trimmed absolute value estimator (Hawkins & Olive, 1999), which has not however acquired attention of practical econometricians. Moreover, to the best of our knowledge, its version with weights implicitly assigned to individual observations seems to be still lacking. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2021 Elektronická adresa https://msed.vse.cz/msed_2020/article/351-Vidnerova-Petra-paper.pdf
Počet záznamů: 1