Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
1.
SYSNO ASEP
0506936
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 (UTIA-B)
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 ; multivariate data ; correlation coefficient ; econometrics
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
UTIA-B - RVO:67985556
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.