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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Journal ArticleDOI
Bootstrap confidence intervals for predicted rainfall quantiles
TL;DR: In this paper, the authors discuss forming confidence intervals on the predicted rainfall quantiles using percentile bootstrap methods to better understand the effect of the Southern Oscillation Index (SOI) phases.
Journal ArticleDOI
Neuronal Responses to Passive Movement in the Globus Pallidus Internus in Primary Dystonia
TL;DR: In this paper, the authors characterized GPi neuronal responses to passive movement of the contralateral limbs in 22 patients with primary dystonia undergoing microelectrode recording for placement of deep brain stimulator leads.
Journal ArticleDOI
Mismatch Between Ectotherm Thermal Preferenda and Optima for Swimming: A Test of the Evolutionary Pace Hypothesis
TL;DR: The hypothesis that a difference in the rates at which Tpref and Topt evolve causes the mismatch in a lineage of European newts is examined, suggesting that the variation in evolutionary rates has a limited potential to modify the disparity between thermal optima and preferenda.
Journal ArticleDOI
Collective efficacy, alcohol outlet density, and young men's alcohol use in rural South Africa.
Hannah H. Leslie,Jennifer Ahern,Audrey Pettifor,Rhian Twine,Kathleen Kahn,F. Xavier Gómez-Olivé,Sheri A. Lippman +6 more
TL;DR: Informal social control and cohesion show protective associations with men's heavy drinking, while alcohol outlet density is associated with more potential problem drinking, providing initial support for intervening at the community level to promote alcohol reduction.
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.