Robust subsampling
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.Download Info
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Bibliographic Info
Article provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 167 (2012)
Issue (Month): 1 ()
Pages: 197-210
Contact details of provider:
Web page: http://www.elsevier.com/locate/jeconom
Related research
Keywords: Subsampling; Bootstrap; Breakdown point; Robustness;Other versions of this item:
- Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2006. "Robust Subsampling," Swiss Finance Institute Research Paper Series 06-33, Swiss Finance Institute.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Loriano Mancini & Fabio Trojani, 2005.
"Robust Value at Risk Prediction,"
Swiss Finance Institute Research Paper Series
07-31, Swiss Finance Institute, revised Oct 2007.
- Loriano Mancini & Fabio Trojani, 2007. "Robust Value at Risk Prediction," University of St. Gallen Department of Economics working paper series 2007 2007-36, Department of Economics, University of St. Gallen.
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