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
Charting the development of emotion comprehension and abstraction from childhood to adulthood using observer-rated and linguistic measures.
Erik C. Nook,Caitlin Stavish,Stephanie F. Sasse,Hilary K. Lambert,Patrick Mair,Katie A. McLaughlin,Leah H. Somerville +6 more
TL;DR: It is suggested that adolescence is a period in which emotion words are comprehended but their level of abstraction continues to mature, and a tool for assessing definitions of emotion terms is provided.
Journal ArticleDOI
Interval Estimation for a Difference Between Intraclass Kappa Statistics
Allan Dormer,Guangyong Zou +1 more
TL;DR: Two methods based on an idea proposed by Newcombe for constructing a confidence interval about a difference between independent kappa statistics that is valid in samples of small to moderate size are proposed and evaluated.
Journal ArticleDOI
Explorations in statistics: the bootstrap.
TL;DR: This fourth installment of Explorations in Statistics explores the bootstrap, an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the sample mean.
Journal ArticleDOI
Jaatha: a fast composite‐likelihood approach to estimate demographic parameters
TL;DR: Jaatha is presented, a new composite‐likelihood method that does incorporate recent divergence and is also applicable when intralocus recombination rates are high, and estimates four demographic parameters for two closely related wild tomato species, Solanum chilense and S. peruvianum.
Journal ArticleDOI
Confidence Interval Estimation for Oscillometric Blood Pressure Measurements Using Bootstrap Approaches
TL;DR: Application of the proposed methodology on an experimental data set of 85 patients with five sets of measurements for each patient has yielded a narrower CI than the currently available conventional methods such as Student's t-distribution method.
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.