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 paper, a general bootstrap method for hypothesis testing is studied, which preserves the data structure of each group independently and the null hypothesis is only used in order to compute the bootstrap statistic values (not at the resampling, as usual).
51 citations
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TL;DR: A model-based resampling technique, based on repeatedly sampling trajectories through the multi-state model, which is motivated by the wish to obtain standard errors of the regression coefficients of the reduced-rank model.
Abstract: In this paper we address two issues arising in multi-state models with covariates. The first issue deals with how to obtain parsimony in the modeling of the effect of covariates. The standard way of incorporating covariates in multi-state models is by considering the transitions as separate building blocks, and modeling the effect of covariates for each transition separately, usually through a proportional hazards model for the transition hazard. This typically leads to a large number of regression coefficients to be estimated, and there is a real danger of over-fitting, especially when transitions with few events are present. We extend the reduced-rank ideas, proposed earlier in the context of competing risks, to multi-state models, in order to deal with this issue. The second issue addressed in this paper was motivated by the wish to obtain standard errors of the regression coefficients of the reduced-rank model. We propose a model-based resampling technique, based on repeatedly sampling trajectories through the multi-state model. The same ideas are also used for the estimation of prediction probabilities in general multi-state models and associated standard errors. We use data from the European Group for Blood and Marrow Transplantation to illustrate our techniques.
51 citations
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TL;DR: This paper discusses resampling methods for finding thresholds in single subject fMRI analysis and shows that the presence of a BOLD response in the time series biases the estimation of the temporal autocorrelation, which in turn leads to biased thresholds.
51 citations
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01 Jan 2002TL;DR: Attention is focused on nonparametric resampling methods of the periodogram and their application to statistical inference in the frequency domain.
Abstract: The paper discusses frequency domain bootstrap methods for time series including some recent developments. Attention is focused on nonparametric resampling methods of the periodogram and their application to statistical inference in the frequency domain.
51 citations
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TL;DR: In this paper, affine-invariant k-variate extensions of the one-sample signed-rank test and the Hodges-Lehmann estimate are considered, and the necessary distribution theory is developed, and asymptotic Pitman efficiencies with respect to Hotelling's T 2 test under multivariate t distributions are tabulated.
Abstract: Brown and Hettmansperger introduced affine-invariant bivariate analogs of the sign, rank, and signed-rank tests based on the Oja median. In this article affine-invariant k-variate extensions of the one-sample signed-rank test and the Hodges-Lehmann estimate are considered. The necessary distribution theory is developed, and asymptotic Pitman efficiencies with respect to Hotelling's T 2 test under multivariate t distributions are tabulated. An application of the signed-rank tests to a repeated-measurement setting is presented.
51 citations