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Multivariate Permutation Tests : With Applications in Biostatistics
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In this article, a simple testing problem for one-sample problems is discussed, and a theory of permutation tests for multisample problems is presented. But this problem is not applicable to the problem of multivariate multi-sample problem.Abstract:
Preface. Notation and Abbreviations. Introduction. Discussion of a Simple Testing Problem. Theory of Permutation Tests for One-Sample Problems. Examples of Univariate Multi-Sample Problems. Theory of Permutation Tests for Multi-Sample Problems. Nonparametric Combination Methodology. Examples of Nonparametric Combination. Permutation Analysis in Factorial Designs. Permutation Testing with Missing Data. The Behrens-Fisher Permutation Problem. Permutation Testing for Repeated Measurements. Further Applications. References. Indexread more
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Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference
TL;DR: A new method is proposed which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems, and is referred to as "threshold-free cluster enhancement" (TFCE).
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Permutation inference for the general linear model.
Anderson M. Winkler,Anderson M. Winkler,Anderson M. Winkler,Gerard R. Ridgway,Matthew A. Webster,Stephen M. Smith,Thomas E. Nichols +6 more
TL;DR: This paper presents a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and shows that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios.
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Controlling the familywise error rate in functional neuroimaging: a comparative review:
TL;DR: It is found that Bonferroni-related tests offer little improvement over Bonferronsi, while the permutation method offers substantial improvement over the random field method for low smoothness and low degrees of freedom.
Journal ArticleDOI
Permutation tests for multi-factorial analysis of variance
TL;DR: In this paper, the authors provide a guideline for constructing an exact permutation strategy, where possible, for any individual term in any ANOVA design, and provide results of Monte Carlo simulations to compare the level accuracy and power of different permutation strategies in two-way ANOVA.
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A Lego System for Conditional Inference
TL;DR: This article reanalyze four datasets by adapting the general conceptual framework to these challenging inference problems and using the coin add-on package in the R system for statistical computing to show what one can gain from going beyond the “classical” test procedures.