Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
1.
SYSNO ASEP
0506936
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 (UTIA-B)
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 ; multivariate data ; correlation coefficient ; econometrics
Vědní obor RIV
BB - Aplikovaná statistika, operační výzkum
Obor OECD
Statistics and probability
CEP
GA17-07384S GA ČR - Grantová agentura ČR
Institucionální podpora
UTIA-B - RVO:67985556
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.