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2. Non-central limit theorems for random selections.

Author

Berkes, István, Horváth, Lajos, and Schauer, Johannes

Subjects
RANDOM sets, STATISTICS, ASYMPTOTES, CENTRAL limit theorem, STATISTICAL bootstrapping
Abstract

Selection from finite sets is a basic procedure of statistics and the partial sum behavior of selected elements is completely known under the “uniform asymptotic negligibility” condition of central limit theory. The purpose of the present paper is to determine the asymptotic behavior of partial sums when the central limit theorem fails. As an application, we describe the limiting properties of permutation and bootstrap statistics in case of infinite variance. [ABSTRACT FROM AUTHOR]

Published
2010
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