Importance of interpolation when constructing double-bootstrap confidence intervals
Date
2000
Authors
Hall, Peter
Lee, S-M
Young, G A
Journal Title
Journal ISSN
Volume Title
Publisher
Aiden Press
Abstract
We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.
Description
Keywords
Keywords: Confidence interval; Coverage error; Edgeworth expansion; Iterated bootstrap; Monte Carlo simulation; Resample; Simulation
Citation
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Source
Journal of the Royal Statistical Society Series B
Type
Journal article