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A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators.
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SYSNO ASEP 0509646 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators. Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Tobišková, Nicole (UIVT-O)
Tichavský, Jan (UIVT-O)Source Title Conference Proceedings. 37th International Conference on Mathematical Methods in Economics 2019. - České Budějovice : University of South Bohemia in České Budějovice, Faculty of Economics, 2019 / Houda M. ; Remeš R. - ISBN 978-80-7394-760-6 Pages s. 168-173 Number of pages 6 s. Publication form Online - E Action MME 2019: International Conference on Mathematical Methods in Economics /37./ Event date 11.09.2019 - 13.09.2019 VEvent location České Budějovice Country CZ - Czech Republic Event type WRD Language eng - English Country CZ - Czech Republic Keywords robustness ; linear regression ; outliers ; bootstrap ; least weighted squares Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA19-05704S GA ČR - Czech Science Foundation (CSF) GA17-01251S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000507570400027 Annotation While various robust regression estimators are available for the standard linear regression model, performance comparisons of individual robust estimators over real or simulated datasets seem to be still lacking. In general, a reliable robust estimator of regression parameters should be consistent and at the same time should have a relatively small variability, i.e. the variances of individual regression parameters should be small. The aim of this paper is to compare the variability of S-estimators, MM-estimators, least trimmed squares, and least weighted squares estimators. While they all are consistent under general assumptions, the asymptotic covariance matrix of the least weighted squares remains infeasible, because the only available formula for its computation depends on the unknown random errors. Thus, we take resort to a nonparametric bootstrap comparison of variability of different robust regression estimators. It turns out that the best results are obtained either with MM-estimators, or with the least weighted squares with suitable weights. The latter estimator is especially recommendable for small sample sizes. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2020
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