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May 2009 Tie-respecting bootstrap methods for estimating distributions of sets and functions of eigenvalues
Peter Hall, Young K. Lee, Byeong U. Park, Debashis Paul
Bernoulli 15(2): 380-401 (May 2009). DOI: 10.3150/08-BEJ154

Abstract

Bootstrap methods are widely used for distribution estimation, although in some problems they are applicable only with difficulty. A case in point is that of estimating the distributions of eigenvalue estimators, or of functions of those estimators, when one or more of the true eigenvalues are tied. The m-out-of-n bootstrap can be used to deal with problems of this general type, but it is very sensitive to the choice of m. In this paper we propose a new approach, where a tie diagnostic is used to determine the locations of ties, and parameter estimates are adjusted accordingly. Our tie diagnostic is governed by a probability level, β, which in principle is an analogue of m in the m-out-of-n bootstrap. However, the tie-respecting bootstrap (TRB) is remarkably robust against the choice of β. This makes the TRB significantly more attractive than the m-out-of-n bootstrap, where the value of m has substantial influence on the final result. The TRB can be used very generally; for example, to test hypotheses about, or construct confidence regions for, the proportion of variability explained by a set of principal components. It is suitable for both finite-dimensional data and functional data.

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Peter Hall. Young K. Lee. Byeong U. Park. Debashis Paul. "Tie-respecting bootstrap methods for estimating distributions of sets and functions of eigenvalues." Bernoulli 15 (2) 380 - 401, May 2009. https://doi.org/10.3150/08-BEJ154

Information

Published: May 2009
First available in Project Euclid: 4 May 2009

zbMATH: 1200.62043
MathSciNet: MR2543867
Digital Object Identifier: 10.3150/08-BEJ154

Rights: Copyright © 2009 Bernoulli Society for Mathematical Statistics and Probability

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Vol.15 • No. 2 • May 2009
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