Bootstrapping weighted empirical processes that do not converge weakly

DOI10.1016/S0167-7152(97)84156-9zbMath1246.62116OpenAlexW2066530436MaRDI QIDQ449905

Soumendra Nath Lahiri

Publication date: 2 September 2012

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

Full work available at URL: https://doi.org/10.1016/s0167-7152(97)84156-9

zbMATH Keywords

weak convergenceconfidence sets

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric tolerance and confidence regions (62G15) Nonparametric statistical resampling methods (62G09) Inference from stochastic processes (62M99)

Cites Work

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• Convergence of empirical processes of mixing rv's on \([0,1\)]
• Bootstrap methods: another look at the jackknife
• Jackknife, bootstrap and other resampling methods in regression analysis
• The weighted empirical process of row independent random variables with arbitrary distribution functions
• Some Convergence Theorems for Ranks and Weighted Empirical Cumulatives
• Weak Convergence of Weighted Empirical Cumulatives Based on Ranks

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