Subsampling, symmetrization, and robust interpolation
Date
2000Author
Politis, Dimitris NicolasRomano, J. P.
Wolf, M.
Source
Communications in Statistics - Theory and MethodsVolume
29Issue
8Pages
1741-1757Google Scholar check
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The recently developed subsampling methodology has been shown to be valid for the construction of large-sample confidence regions for a general unknown parameter θ under very minimal conditions. Nevertheless, in some specific cases - e.g. in the case of the sample mean of i.i.d. data - it has been noted that the subsampling distribution estimator underperforms as compared to alternative estimators such as the bootstrap or the asymptotic normal distribution (with estimated variance). In the present report we introduce a (partially) symmetrized subsampling distribution estimator we then show that the new estimator is higher-order accurate under the standard regularity conditions, while at the same time retaining the robustness property of consistent distribution estimation even in nonregular cases. Both i.i.d. and weakly dependent (mixing) observations are considered. Copyright © 2000 by Marcel Dekker, Inc.