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
More filters
Journal ArticleDOI
Effects of Egg and Circulating Testosterone on Ring‐Necked Pheasant (Phasianus colchicus) Male Traits and Combat Outcome
Diego Rubolini,Leonida Fusani,Andrea Bonisoli-Alquati,Virginie Canoine,Manuela Caprioli,Maria Romano,Roberto Ambrosini,Francesco Dessì-Fulgheri,Nicola Saino +8 more
TL;DR: It is suggested that the long-term effects of egg T on male phenotype do not originate from differential gonadal maturation according to egg T treatment, and prenatal androgens may have priming effects on functioning of target tissues, translating into differential phenotypic effects according to androgen exposure during embryonic development.
Journal ArticleDOI
Probabilistic Modeling and Bayesian Filtering for Improved State Estimation for Soft Robots
TL;DR: A method of estimating real-time states of soft robots by filtering noisy output signals and including hysteresis in the models using a Bayesian network and shows significant improvement in state estimation compared to conventional estimation methods.
Journal ArticleDOI
On estimation and diagnostics analysis in log-generalized gamma regression model for interval-censored data
TL;DR: In this article, a location-scale regression model based on the log-generalized gamma distribution is proposed for modeling interval-censored data, where the event of interest is not observed exactly but it is only known to occur within some time interval.
Journal ArticleDOI
The DURATIONS randomised trial design: Estimation targets, analysis methods and operating characteristics
TL;DR: The operating characteristics (type-1 and type-2 errors) of different statistical methods of drawing inference from the estimated curve are investigated to show that the bootstrap approach has good operating characteristics in a wide range of scenarios and can be used with confidence by researchers wishing to design a DURATIONS trial to reduce treatment duration.
ReportDOI
Reliability of confidence intervals calculated by bootstrap and classical methods using the FIA 1-ha plot design
H. T. Schreuder,M. S. Williams +1 more
TL;DR: In simulation sampling from forest populations using sample sizes of 20, 40, and 60 plots respectively, confidence intervals based on the bootstrap (accelerated, percentile, and t-distribution based) were calculated and compared with thosebased on the classical t confidence intervals for mapped populations and subdomains within those populations.
References
More filters
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.