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: In this article, the authors introduced a method of multiple hypothesis testing that combines the idea of sequential multiple testing procedures with the structure of resampling methods, which can be seen as an alternative to the analytic method of Dunnett and Tamhane, which requires a specific distributional form.
Abstract: This article introduces a method of multiple hypothesis testing that combines the idea of sequential multiple testing procedures with the structure of resampling methods. The method can be seen as an alternative to the analytic method of Dunnett and Tamhane, which requires a specific distributional form. Resampling incorporates the covariance structure of the data without the need for distributional assumptions. Recent work by Westfall and Young has shown that a step-down resampling method is asymptotically consistent when adjusted p values can be obtained exactly for continuous data. This article shows that in the case of a comparison of two groups on multiple outcomes, those results are generalizable to discrete data where exact adjusted p values are not available. It is shown that the method asymptotically attains the desired level for controlling the experimentwise probability of a type I error.
78 citations
TL;DR: Pattern jitter is a resampling technique that accomplishes this by preserving the recent spiking history of all spikes and constraining resampled spikes to remain close to their original positions.
Abstract: Resampling methods are popular tools for exploring the statistical structure of neural spike trains. In many applications, it is desirable to have resamples that preserve certain non-Poisson properties, like refractory periods and bursting, and that are also robust to trial-to-trial variability. Pattern jitter is a resampling technique that accomplishes this by preserving the recent spiking history of all spikes and constraining resampled spikes to remain close to their original positions. The resampled spike times are maximally random up to these constraints. Dynamic programming is used to create an efficient resampling algorithm.
78 citations
TL;DR: In this paper, it is shown that for a certain "local" parameter set where the signal to noise ratio is small, it is asymptotically possible to estimate the linear model parameters using the partial likelihood as well as if the transformation were known.
Abstract: Estimates of the linear model parameters in a linear transformation model with unknown increasing transformation are obtained by maximizing a partial likelihood. A resampling scheme (likelihood sampler) is used to compute the maximum partial likelihood estimates. It is shown that for a certain "local" parameter set where the "signal to noise ratio" is small, it is asymptotically possible to estimate the linear model parameters using the partial likelihood as well as if the transformation were known. In the case of the power transformation model with symmetric error distribution, this result is shown to also hold when the distribution of the error in the transformed linear model is unknown and is estimated. Monte Carlo results are used to show that for moderate sample size and small to moderate signal to noise ratio, the asymptotic results are approximately in effect and thus the partial likelihood estimates perform very well. Estimates of the transformation are introduced and it is shown that the estimates, when centered at the transformation and multiplied by $\sqrt{n}$, converge weakly to Gaussian processes.
78 citations
31 Mar 2010
TL;DR: In this paper, the authors address the problem of estimating the autocovariance matrix of a stationary process under short range dependence assumptions, and establish convergence rates for a gradually tapered version of the sample auto-correlation matrix and for its inverse.
Abstract: We address the problem of estimating the autocovariance matrix of a stationary process. Under short range dependence assumptions, convergence rates are established for a gradually tapered version of the sample autocovariance matrix and for its inverse. The proposed estimator is formed by leaving the main diagonals of the sample autocovariance matrix intact while gradually down-weighting o�-diagonal entries towards zero. In addition we show the same convergence rates hold for a positive de�nite version of the estimator, and we introduce a new approach for selecting the banding parameter. The new matrix estimator is shown to perform well theoretically and in simulation studies. As an application we introduce a new resampling scheme for stationary processes termed the linear process bootstrap (LPB). The LPB is shown to be asymptotically valid for the sample mean and related statistics. The e�ectiveness of the proposed methods are demonstrated in a simulation study.
77 citations
TL;DR: In this article, a new statistical quantity, the energy, is introduced to test whether two samples originate from the same distribution, which is a simple logarithmic function of the distances of the observations in the variate space.
Abstract: We introduce a new statistical quantity, the energy, to test whether two samples originate from the same distributions. The energy is a simple logarithmic function of the distances of the observations in the variate space. The distribution of the test statistic is determined by a resampling method. The power of the energy test in one dimension was studied for a variety of different test samples and compared to several nonparametric tests. In two and four dimensions, a comparison was performed with the Friedman–Rafsky and nearest neighbor tests. The two-sample energy test was shown to be especially powerful in multidimensional applications.
77 citations