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Biometrika 1987 74(1):85-93; doi:10.1093/biomet/74.1.85
© 1987 by Biometrika Trust
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Conditional bootstrap methods in the mean-shift model

DAVID HINKLEY and EDNA SCHECHTMAN

Center for Statistical Sciences, University of Texas Austin, Texas 78712, U.S.A.

Bootstrap methods are not inherently conditional, but they can be made so by appropriate stratification of the simulated samples which bootstrap produces. We show how stratification can work in a bootstrap analysis of mean-shift in Nile river flow data. The results are compared with both parametric and semiparametric likelihood analyses. The paper ends with some general remarks on conditional bootstraps.

Key Words: Ancillary statistic • Bootstrap • Change-point model • Density estimation • Empirical likelihood • Logistic regression • Semiparametric inference


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