Additions and correction to ``The bootstrap of the mean with arbitrary bootstrap sample size

zbMath0747.62019MaRDI QIDQ1185280

Miguel A. Arcones, Evarist Giné M.

Publication date: 28 June 1992

Published in: Annales de l'Institut Henri Poincaré. Probabilités et Statistiques (Search for Journal in Brave)

Full work available at URL: http://www.numdam.org/item?id=AIHPB_1991__27_4_583_0

zbMATH Keywords

correctionbootstrap central limit theoremadditions\(p\)-stable lawconvergence of bootstrap momentsdomain of attraction caseself-normalized limit theorems

Mathematics Subject Classification ID

Infinitely divisible distributions; stable distributions (60E07) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09) Asymptotic properties of parametric tests (62F05)

Related Items (16)

Bootstrapping the Student \(t\)-statistic ⋮ On bootstrap estimation of the distribution of the Studentized mean ⋮ Estimation of the mean for critical branching process and its bootstrap approximation ⋮ Wild Bootstrap of the Sample Mean in the Infinite Variance Case ⋮ The limit distribution of the bootstrap for the unit root test statistic when the residuals are dependent ⋮ On the low intensity bootstrap for triangular arrays of independent identically distributed random variables ⋮ On the validity of the residual-based bootstrap for the unit root test statistic with long memory observations ⋮ How do bootstrap and permutation tests work? ⋮ Asymptotics for the residual-based bootstrap approximation in nearly nonstationary AR(1) models with possibly heavy-tailed innovations ⋮ Bootstrapping weighted empirical processes that do not converge weakly ⋮ A bootstrap approximation to a unit root test statistic for heavy-tailed observations. ⋮ LINK OF MOMENTS BEFORE AND AFTER TRANSFORMATIONS, WITH AN APPLICATION TO RESAMPLING FROM FAT-TAILED DISTRIBUTIONS ⋮ Test for the existence of finite moments via bootstrap ⋮ Robust Inference Using Inverse Probability Weighting ⋮ UNIT ROOT INFERENCE FOR NON-STATIONARY LINEAR PROCESSES DRIVEN BY INFINITE VARIANCE INNOVATIONS ⋮ Resampling schemes with low resampling intensity and their applications in testing hypotheses

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