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Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
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SYSNO ASEP 0497296 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID Zdroj.dok. The 12th International Days of Statistics and Economics Conference Proceedings. - Slaný : Melandrium, 2018 / Löster T. ; Pavelka T. - ISBN 978-80-87990-14-8 Rozsah stran s. 770-779 Poč.str. 10 s. Forma vydání Tištěná - P Akce International Days of Statistics and Economics /12./ Datum konání 06.09.2018 - 08.09.2018 Místo konání Prague Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova robust statistics ; econometrics ; correlation coefficient ; multivariate data Vědní obor RIV BB - Aplikovaná statistika, operační výzkum Obor OECD Statistics and probability CEP GA17-07384S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000455809400077 Anotace The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation coefficient based on the least weighted squares regression and the minimum weighted covariance determinant estimator, where the latter allows to estimate the mean and covariance matrix of multivariate data. New tools are proposed allowing to test hypotheses about these robust estimators or to estimate their variance. The techniques considered in the paper include resampling approaches with or without replacement, i.e. permutation tests, bootstrap variance estimation, and bootstrap confidence intervals. The performance of the newly described tools is illustrated on numerical examples. They reveal the suitability of the robust procedures also for non-contaminated data, as their confidence intervals are not much wider compared to those for standard maximum likelihood estimators. While resampling without replacement turns out to be more suitable for hypothesis testing, bootstrapping with replacement yields reliable confidence intervals but not corresponding hypothesis tests. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2019 Elektronická adresa https://msed.vse.cz/msed_2018/article/3-Kalina-Jan-paper.pdf
Počet záznamů: 1