Robust subsampling
Journal
Journal of Econometrics
ISSN
0304-4076
ISSN-Digital
1872-6895
Type
journal article
Date Issued
2012-03-13
Author(s)
Camponovo, Lorenzo
;
Scaillet, Olivier
;
Trojani, Fabio
Abstract
We characterize the robustness of subsampling procedures by deriving a formula for the breakdown point of subsampling quantiles. This breakdown point can be very low for moderate subsampling block sizes, which implies the fragility of subsampling procedures, even when they are applied to robust statistics. This instability arises also for data driven block size selection procedures minimizing the minimum confidence interval volatility index, but can be mitigated if a more robust calibration method can be applied instead. To overcome these robustness problems, we introduce a consistent robust subsampling procedure for M-estimators and derive explicit subsampling quantile breakdown point characterizations for MM-estimators in the linear regression model. Monte Carlo simulations in two settings where the bootstrap fails show the accuracy and robustness of the robust subsampling relative to the subsampling.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Elsevier
Publisher place
Amsterdam
Volume
167
Number
1
Start page
197
End page
210
Pages
14
Subject(s)
Division(s)
Eprints ID
210467