Topic
Resampling
About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.
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TL;DR: A cluster methodology, motivated via density estimation, is proposed, based on the idea of estimating the population clusters, which are defined as the connected parts of the “substantial” support of the underlying density.
127 citations
TL;DR: In this paper, Bayesian analyses of traditional normal-mixture models for classification and discrimination are discussed, which involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling.
Abstract: We discuss Bayesian analyses of traditional normal-mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal-mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second-level uncertainties in classification.
127 citations
TL;DR: The objective is to produce two‐ or three‐dimensional brain maps that provide, at each pixel in the map, an estimated P value with absolute meaning, that is, each P value approximates the probability of having obtained by chance the observed signal effect at that pixel, given that the null hypothesis is true.
Abstract: Although functional magnetic resonance imaging (fMRI) methods yield rich temporal and spatial data for even a single subject, universally accepted data analysis techniques have not been developed that use all the potential information from fMRI of the brain. Specifically, temporal correlations and confounds are a problem in assessing change within pixels. Spatial correlations across pixels are a problem in determining regions of activation and in correcting for multiple significance tests. We propose methods that address these issues in the analysis of task-related changes in mean signal intensity for individual subjects. Our approach to temporally based problems within pixels is to employ a model based on autoregressive-moving average (ARMAor ''Box-Jenkins'') time series methods, which we call CARMA (Contrasts and ARMA). To adjust for performing multiple significance tests across pixels, taking into account between-pixel correlations, we propose adjustment of P values with ''resampling methods.'' Our objective is to produce two- or three-dimensional brain maps that provide, at each pixel in the map, an estimated P value with absolute meaning. That is, each P value approximates the probability of having obtained by chance the observed signal effect at that pixel, given that the null hypothesis is true. Simulated and real data examples are provided.Hum. Brain Mapping 5:168-193, 1997. r 1997 Wiley-Liss, Inc.
127 citations
TL;DR: In this paper, a generalized bootstrap technique for estimators obtained by solving estimating equations is introduced. But the use of the proposed technique is discussed in some examples, and distributional consistency of the method is established and an asymptotic representation of the resampling variance estimator is obtained.
Abstract: We introduce a generalized bootstrap technique for estimators obtained by solving estimating equations. Some special cases of this generalized bootstrap are the classical bootstrap of Efron, the delete-d jackknife and variations of the Bayesian bootstrap. The use of the proposed technique is discussed in some examples. Distributional consistency of the method is established and an asymptotic representation of the resampling variance estimator is obtained.
127 citations
TL;DR: Two nonparametric methods and their adaptations to bioavailability ratios are reviewed, one based on Wilcoxon's signed rank test (Tukey), and the other on Pitman's permutation test.
Abstract: For a two-way cross-over design, which appears to be the most common experimental design in bioavailability studies, 95%-confidence limits for expected bioavailability can be obtained by classical analysis of variance (ANOVA). If symmetry of the confidence interval is desired about zero (differences) or unity (ratios) rather than about the corresponding point estimator, Westlake's modification can be used. Two nonparametric methods and their adaptations to bioavailability ratios are reviewed, one based on Wilcoxon's signed rank test (Tukey), and the other on Pitman's permutation test. The necessary assumptions and the merits of these procedures are discussed. The methods are illustrated by an example of a comparative bioavailability study. A FORTRAN program facilitating the procedures is available from the authors upon request.
127 citations