bibtype C - Conference Paper (international conference)
ARLID 0506936
utime 20240103222329.1
mtime 20190726235959.9
WOS 000455809400077
title (primary) (eng) Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
specification
page_count 10 s.
media_type P
serial
ARLID cav_un_epca*0497295
ISBN 978-80-87990-14-8
title The 12th International Days of Statistics and Economics Conference Proceedings
page_num 770-779
publisher
place Slaný
name Melandrium
year 2018
editor
name1 Löster
name2 T.
editor
name1 Pavelka
name2 T.
keyword robust statistics
keyword multivariate data
keyword correlation coefficient
keyword econometrics
author (primary)
ARLID cav_un_auth*0345793
name1 Kalina
name2 Jan
full_dept (cz) Stochastická informatika
full_dept (eng) Department of Stochastic Informatics
department (cz) SI
department (eng) SI
institution UTIA-B
full_dept Department of Stochastic Informatics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2019/SI/kalina-0506936.pdf
cas_special
project
ARLID cav_un_auth*0345381
project_id GA17-07384S
agency GA ČR
abstract (eng) 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.
action
ARLID cav_un_auth*0368169
name International Days of Statistics and Economics /12./
dates 20180906
mrcbC20-s 20180908
place Prague
country CZ
RIV BB
FORD0 10000
FORD1 10100
FORD2 10103
reportyear 2020
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0298063
confidential S
mrcbC86 n.a. Proceedings Paper Economics|Social Sciences Interdisciplinary
mrcbC86 n.a. Proceedings Paper Economics|Social Sciences Interdisciplinary
mrcbC86 n.a. Proceedings Paper Economics|Social Sciences Interdisciplinary
arlyear 2018
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 000455809400077 WOS
mrcbU63 cav_un_epca*0497295 The 12th International Days of Statistics and Economics Conference Proceedings 978-80-87990-14-8 770 779 Slaný Melandrium 2018
mrcbU67 340 Löster T.
mrcbU67 340 Pavelka T.