Asymptotics for multivariate \(t\)-statistic for random vectors in the generalized domain of attraction of the multivariate normal law

DOI10.1016/0167-7152(95)00217-0zbMath0862.62016OpenAlexW2035303022MaRDI QIDQ1126116

Steven J. Sepanski

Publication date: 29 May 1997

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(95)00217-0

zbMATH Keywords

bootstrapasymptotic normalitycentral limit theoremgeneralized domain of attractionStudent's \(t\)-statisticaffine normalizationself normalization

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05)

Related Items (6)

Halfline tests for multivariate one-sided alternatives ⋮ Sampling distributions associated with the multivariate t distribution ⋮ Empirical likelihood ratio under infinite covariance matrix of the random vectors ⋮ Empirical likelihood ratio for two-sample compound Poisson processes under infinite second moment ⋮ Empirical likelihood ratio under infinite second moment for two-sample problems ⋮ Invariance principles for a multivariate Student process in the generalized domain of attraction of the multivariate normal law

Cites Work

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• Matrix normalization of sums of random vectors in the domain of attraction of the multivariate normal
• Quadratic negligibility and the asymptotic normality of operator normed sums
• Asymptotics for multivariate \(t\)-statistic and Hotelling's \(T^ 2\)- statistic under infinite second moments via bootstrapping
• Necessary and sufficient conditions for the multivariate bootstrap of the mean
• Probabilistic characterizations of the generalized domain of attraction of the multivariate normal law
• On the asymptotic normality of self-normalized sums
• Matrix Analysis
• Convergence of types in k-space

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