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Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
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SYSNO ASEP 0497296 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID Source Title The 12th International Days of Statistics and Economics Conference Proceedings. - Slaný : Melandrium, 2018 / Löster T. ; Pavelka T. - ISBN 978-80-87990-14-8 Pages s. 770-779 Number of pages 10 s. Publication form Print - P Action International Days of Statistics and Economics /12./ Event date 06.09.2018 - 08.09.2018 VEvent location Prague Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic Keywords robust statistics ; econometrics ; correlation coefficient ; multivariate data Subject RIV BB - Applied Statistics, Operational Research OECD category Statistics and probability R&D Projects GA17-07384S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000455809400077 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2019 Electronic address https://msed.vse.cz/msed_2018/article/3-Kalina-Jan-paper.pdf
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