Do applied statisticians prefer more randomness or less? Bootstrap or jackknife?

DOI10.1007/S11222-024-10388-7zbMATH Open1539.62037MaRDI QIDQ6547742

Yannis G. Yatracos

Publication date: 31 May 2024

Published in: Statistics and Computing (Search for Journal in Brave)

zbMATH Keywords

variabilitymean squared errorinadmissibilitybig databootstrap asymptoticsrisk averse decision maker

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Asymptotic distribution theory in statistics (62E20) Nonparametric statistical resampling methods (62G09)

Cites Work

• Jackknife approximations to bootstrap estimates
• On the number of bootstrap simulations required to construct a confidence interval
• Bootstrap methods: another look at the jackknife
• Jackknife, bootstrap and other resampling methods in regression analysis
• Assessing the quality of bootstrap samples and of the bootstrap estimates obtained with finite resampling.
• Inference from small and big data sets with error rates
• Bootstrap and wild bootstrap for high dimensional linear models
• Intentionally Biased Bootstrap Methods
• Artificially Augmented Samples, Shrinkage, and Mean Squared Error Reduction*

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