Bootstrapping the Student \(t\)-statistic
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Publication:1872230
DOI10.1214/aop/1015345757zbMath1010.62026OpenAlexW2036565019MaRDI QIDQ1872230
Publication date: 6 May 2003
Published in: The Annals of Probability (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aop/1015345757
Asymptotic properties of parametric estimators (62F12) Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05) Bootstrap, jackknife and other resampling methods (62F40)
Related Items (10)
A central limit theorem for bootstrap sample sums from non-i.i.d. models ⋮ Precise asymptotics in the deviation probability series of self-normalized sums ⋮ How do bootstrap and permutation tests work? ⋮ Weighted resampling of martingale difference arrays with applications ⋮ Moment condition tests for heavy tailed time series ⋮ Another look at bootstrapping the Student \(t\)-statistic ⋮ A Monte-Carlo comparison of Studentized bootstrap and permutation tests for heteroscedastic two-sample problems ⋮ Resampling Student's \(t\)-type statistics ⋮ The asymptotic distribution of self-normalized triangular arrays ⋮ Inference from small and big data sets with error rates
Cites Work
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- The bootstrap of the mean with arbitrary bootstrap sample size
- Asymptotic properties of the bootstrap for heavy-tailed distributions
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- Necessary conditions for the bootstrap of the mean
- Additions and correction to ``The bootstrap of the mean with arbitrary bootstrap sample size
- Some results on the influence of extremes on the bootstrap
- When is the Student \(t\)-statistic asymptotically standard normal?
- Limiting Behaviour of Sums and the Term of Maximum Modulus
- A limit theorem for sample maxima and heavy branches in Galton–Watson trees
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