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
Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity.
Sharon Chiang,Emilian R. Vankov,Hsiang J. Yeh,Michele Guindani,Marina Vannucci,Zulfi Haneef,John M. Stern +6 more
TL;DR: This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.
Proceedings ArticleDOI
ErpICASSO: A tool for reliability estimates of independent components in EEG event-related analysis
Fiorenzo Artoni,Angelo Gemignani,Laura Sebastiani,Remo Bedini,Alberto Landi,Danilo Menicucci +5 more
TL;DR: ErdpICASSO is presented, a new method which modifies a data-driven approach named ICASSO for the analysis of trials (epochs), and provides a quality index of each extracted ERP component by combining trial-to-trial bootstrapping and CCA projection.
Journal ArticleDOI
Tales of emotion and stock in China: volatility, causality and prediction
Zhenkun Zhou,Ke Xu,Jichang Zhao +2 more
TL;DR: Wang et al. as mentioned in this paper investigated how the online social media, like Twitter or its variant Weibo, interacts with the stock market and whether it can be a convincing proxy to predict stock market, and revealed that inexperienced investors with high emotional volatility are more sensible to the market fluctuations than the experienced or institutional ones.
Journal ArticleDOI
Brain responses to emotional stimuli during breath holding and hypoxia: an approach based on the independent component analysis.
Danilo Menicucci,Fiorenzo Artoni,Remo Bedini,Alessandro Pingitore,Mirko Passera,Alberto Landi,Antonio L'Abbate,Laura Sebastiani,Angelo Gemignani,Angelo Gemignani +9 more
TL;DR: The reduction of unpleasant-related ERP components suggests that the evaluation of aversive and/or possibly dangerous situations might be altered during breath holding.
Journal ArticleDOI
Computing Social Value Conversion in the Human Brain
Haruaki Fukuda,Haruaki Fukuda,Ning Ma,Shinsuke Suzuki,Shinsuke Suzuki,Norihiro Harasawa,Kenichi Ueno,Justin L. Gardner,Noritaka Ichinohe,Masahiko Haruno,Kang Cheng,Hiroyuki Nakahara +11 more
TL;DR: It is demonstrated that the variability in the conversion underlies the difference between prosocial and selfish subjects, as seen from the differential strength of the rAI and ldlPFC coupling to the mPFC responses, respectively.
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.