Edgeworth expansions in very-high-dimensional problems

DOI10.1016/S0378-3758(97)00171-7zbMath0942.62017OpenAlexW1981411292MaRDI QIDQ1299406

Niall H. Anderson, Hall, Peter, Michael D. Titterington

Publication date: 21 August 2000

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(97)00171-7

zbMATH Keywords

central limit theoremfunctional data analysisBerry-Esseen theorem

Mathematics Subject Classification ID

Multivariate analysis (62H99) Asymptotic distribution theory in statistics (62E20)

Related Items (5)

Nearly optimal central limit theorem and bootstrap approximations in high dimensions ⋮ On properties of percentile bootstrap confidence intervals for prediction in functional linear regression ⋮ Second- and higher-order Gaussian anticoncentration inequalities and error bounds in Slepian's comparison theorem ⋮ High-dimensional central limit theorems by Stein's method ⋮ Central limit theorem and bootstrap approximation in high dimensions: near \(1/\sqrt{n}\) rates via implicit smoothing

Cites Work

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• Dependence of the Berry-Esseen estimate on the dimension
• Statistical tools to analyze data representing a sample of curves
• On the Convergence Rate in the Infinite-Dimensional Central Limit Theorem for Probabilities of Hitting Parallelepipeds
• Uniform Estimates of the Rate of Convergence in the Multi-Dimensional Central Limit Theorem
• Asymptotic expansions for bivariate von Mises functionals

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