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A Bootstrap Comparison of Robust Regression Estimators

    1.
    SYSNO ASEP0583572
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleA Bootstrap Comparison of Robust Regression Estimators
    Author(s) Kalina, Jan (UTIA-B)
    Janáček, Patrik (UTIA-B)
    Source TitleMathematical Methods in Economics 2022: Proceedings. - Jihlava : College of Polytechnics Jihlava, 2022 / Vojáčková H. - ISBN 978-80-88064-62-6
    Pagess. 161-167
    Number of pages7 s.
    Publication formPrint - P
    ActionMME 2022: International Conference on Mathematical Methods in Economics /40./
    Event date07.09.2022 - 09.09.2022
    VEvent locationJihlava
    CountryCZ - Czech Republic
    Event typeEUR
    Languageeng - English
    CountryCZ - Czech Republic
    Keywordslinear regression ; robust estimation ; nonparametric bootstrap ; bootstrap hypothesis testing
    Subject RIVBA - General Mathematics
    OECD categoryStatistics and probability
    R&D ProjectsGA21-05325S GA ČR - Czech Science Foundation (CSF)
    Institutional supportUTIA-B - RVO:67985556
    UT WOS000936355000066
    AnnotationThe ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more preferable alternatives.
    WorkplaceInstitute of Information Theory and Automation
    ContactMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Year of Publishing2024
Number of the records: 1  

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