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Least Weighted Absolute Value Estimator with an Application to Investment Data
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SYSNO ASEP 0535711 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Least Weighted Absolute Value Estimator with an Application to Investment Data Author(s) Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Kalina, Jan (UIVT-O) RID, SAI, ORCIDSource Title The 14th International Days of Statistics and Economics Conference Proceedings. - Slaný : Melandrium, 2020 / Löster T. ; Pavelka T. - ISBN 978-80-87990-22-3 Pages s. 1357-1366 Number of pages 10 s. Publication form Print - P Action International Days of Statistics and Economics /14./ Event date 10.09.2020 - 12.09.2020 VEvent location Prague Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic Keywords robust regression ; regression median ; implicit weighting ; computational aspects ; nonparametric bootstrap Subject RIV BB - Applied Statistics, Operational Research OECD category Statistics and probability R&D Projects GA18-23827S GA ČR - Czech Science Foundation (CSF) GA19-05704S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021 Electronic address https://msed.vse.cz/msed_2020/article/351-Vidnerova-Petra-paper.pdf
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