New Edgeworth-type expansions with finite sample guarantees

DOI10.1214/22-AOS2192MaRDI QIDQ2105182

Mayya Zhilova

Publication date: 8 December 2022

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.03959

zbMATH Keywords

bootstrapEdgeworth seriesmodel misspecificationfinite sample inferencechi-square approximationhigher-order accuracylinear contrastsdependence on dimensionanticoncentration inequalitybootstrap score testelliptic confidence setsmultivariate Berry-Esseen inequality

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25) Bootstrap, jackknife and other resampling methods (62F40) Approximations to statistical distributions (nonasymptotic) (62E17)

Uses Software

• bootstrap

Cites Work

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• Elliptical insights: understanding statistical methods through elliptical geometry
• Operator norm inequalities between tensor unfoldings on the partition lattice
• Some nonasymptotic results on resampling in high dimension. I: Confidence regions
• A generalization of the Lindeberg principle
• On the central limit theorem in \(R^ p\) when p\(\rightarrow \infty\)
• Bootstrap procedures under some non-i.i.d. models
• Bootstrap methods: another look at the jackknife
• The reverse isoperimetric problem for Gaussian measure
• Exchangeably weighted bootstraps of the general empirical process
• Series approximation methods in statistics
• The weighted bootstrap
• Resampling methods for dependent data
• On the dependence of the Berry-Esseen bound on dimension
• Nonclassical Berry-Esseen inequalities and accuracy of the bootstrap
• Comparison and anti-concentration bounds for maxima of Gaussian random vectors
• Bootstrap and wild bootstrap for high dimensional linear models
• Central limit theorems and bootstrap in high dimensions
• Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
• On Measuring Sensitivity to Parametric Model Misspecification
• Normal Approximation and Asymptotic Expansions
• Science and Statistics
• On Multivariate Edgeworth Expansions
• An elementary proof of the central limit theorem
• Maximum Likelihood Estimation of Misspecified Models
• The bootstrap and Edgeworth expansion