eng
Camponovo, Lorenzo
Scaillet, Olivier
Trojani, Fabio
Robust subsampling
http://doc.rero.ch/record/28644/files/trojani_JE_2012.pdf
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 robustsubsampling 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 robustsubsampling relative to the subsampling.
2012-03-05T21:46:12Z
http://doc.rero.ch/record/28644