On the relative performance of bootstrap and Edgeworth approximations of a distribution function
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Publication:918081
DOI10.1016/0047-259X(90)90019-EzbMath0705.62045MaRDI QIDQ918081
Publication date: 1990
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
bootstrapskewnesscomparison of relative errorsestimating tail probabilitiesgeneral Edgeworth approximationsmoderate deviation probabilityone-term Edgeworth correction
Nonparametric estimation (62G05) Nonparametric statistical resampling methods (62G09) Large deviations (60F10)
Related Items (11)
A parametric bootstrap test for cycles ⋮ Asymptotic expansion in the large deviation zones of the distribution function of a random variable with a regular behavior of its semi-invariants ⋮ Large deviation principle for moderate deviation probabilities of bootstrap empirical measures ⋮ Cramér's type results for some bootstrapped \(U\)-statistics ⋮ Self-normalized Cramér-type large deviations for independent random variables. ⋮ Large deviations of bootstrapped \(U\)-statistics ⋮ A Nonparametric Test for Weak Dependence Against Strong Cycles and its Bootstrap Analogue ⋮ A large deviation principle for bootstrapped sample means ⋮ Bootstrap Sample Size in Nonregular Cases ⋮ On the relative performance of the block bootstrap for dependent data ⋮ Bounds for probabilities of small relative errors for empirical saddlepoint and bootstrap tail approximations
Cites Work
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- Inverting an Edgeworth expansion
- Inference on means using the bootstrap
- Asymptotic expansions for probabilities of large deviations
- Theoretical comparison of bootstrap confidence intervals
- Second order and \(L^ p\)-comparisons between the boostrap and empirical Edgeworth expansion methodologies
- On large deviations for sums of random vectors in \(R_ k\).
- On the asymptotic accuracy of Efron's bootstrap
- Bootstrap methods: another look at the jackknife
- [https://portal.mardi4nfdi.de/wiki/Publication:4140066 Asymptotische Entwicklungen f�r Wahrscheinlichkeiten gro�er Abweichungen]
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