Bootstrap Confidence Intervals
Thomas J. DiCiccio,Bradley Efron +1 more
TLDR
Bootstrap methods for estimating confidence intervals have been surveyed in this article, with a focus on improving the accuracy of the standard confidence intervals in a way that allows routine application even to very complicated problems.Abstract:
This article surveys bootstrap methods for producing good approximate confidence intervals. The goal is to improve by an order of magnitude upon the accuracy of the standard intervals $\hat{\theta} \pm z^{(\alpha)} \hat{\sigma}$, in a way that allows routine application even to very complicated problems. Both theory and examples are used to show how this is done. The first seven sections provide a heuristic overview of four bootstrap confidence interval procedures: $BC_a$, bootstrap-t , ABC and calibration. Sections 8 and 9 describe the theory behind these methods, and their close connection with the likelihood-based confidence interval theory developed by Barndorff-Nielsen, Cox and Reid and others.read more
Citations
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Ethnicity and Contemporary American Culture A Meta-Analytic Investigation of Horizontal–Vertical Individualism–Collectivism
TL;DR: This article found no ethnic/racial differences in the mean scores of both variants of collectivism, although European Americans were higher in vertical individualism than African Americans and Latino Americans, and analyses of the intercorrelations between the four dimensions of individualism revealed noticeable group differences.
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A Global Drought and Flood Catalogue from 1950 to 2016
TL;DR: In this paper, the authors discuss the impacts of hydrological extremes, in the form of droughts and floods, on a wide range of sectors including water availability, food security, and energy production.
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The estimating function bootstrap
Feifang Hu,John D. Kalbfleisch +1 more
TL;DR: In this article, the authors propose a bootstrap procedure which estimates the distribution of an estimating function by resampling its terms using bootstrap techniques, which can be applied to a wide class of practical problems where data are independent but not necessarily identically distributed.
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Bootstrap approach for constructing confidence intervals for population pharmacokinetic parameters. I: A use of bootstrap standard error.
Akifumi Yafune,Makio Ishiguro +1 more
TL;DR: A bootstrap standard error approach for constructing confidence intervals for population pharmacokinetic parameters is proposed and comparisons between the asymptotic and bootstrap confidence intervals are made through applications to a simulated data set and an actual phase I trial.
References
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Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Journal ArticleDOI
Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Book
Bootstrap Methods and Their Application
Anthony C. Davison,David Hinkley +1 more
TL;DR: In this paper, a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis, is given, along with a disk of purpose-written S-Plus programs for implementing the methods described in the text.
Journal ArticleDOI
Better Bootstrap Confidence Intervals
TL;DR: In this article, the authors consider the problem of setting approximate confidence intervals for a single parameter θ in a multiparameter family, and propose a method to automatically incorporate transformations, bias corrections, and so on.
Beiter Bootstrap Confidence Intervals
TL;DR: In this article, the authors consider the problem of setting approximate confidence intervals for a single parameter 0 in a multiparameter family, and propose the bootstrap confidence intervals that automatically incorporate transformations, bias corrections, and so forth.