Robust subsampling

Lorenzo Camponovo, Olivier Scaillet and Fabio Trojani ()

Journal of Econometrics, 2012, vol. 167, issue 1, 197-210

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.

Keywords: Subsampling; Bootstrap; Breakdown point; Robustness (search for similar items in EconPapers)
JEL-codes: C12 C13 C15 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Robust Subsampling (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:167:y:2012:i:1:p:197-210

DOI: 10.1016/j.jeconom.2011.11.005

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