Počet záznamů: 1
A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators.
- 1.
SYSNO ASEP 0509646 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators. Tvůrce(i) Kalina, Jan (UIVT-O) RID, SAI, ORCID
Tobišková, Nicole (UIVT-O)
Tichavský, Jan (UIVT-O)Zdroj.dok. 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 Rozsah stran s. 168-173 Poč.str. 6 s. Forma vydání Online - E Akce MME 2019: International Conference on Mathematical Methods in Economics /37./ Datum konání 11.09.2019 - 13.09.2019 Místo konání České Budějovice Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova robustness ; linear regression ; outliers ; bootstrap ; least weighted squares Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA19-05704S GA ČR - Grantová agentura ČR GA17-01251S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000507570400027 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020
Počet záznamů: 1