Wilcox, R. R., Rousselet, G. A. and Pernet, C. R. (2018) Improved methods for making inferences about multiple skipped correlations. Journal of Statistical Computation and Simulation, 88(16), pp. 3116-3131. (doi: 10.1080/00949655.2018.1501051)
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Abstract
A skipped correlation has the advantage of dealing with outliers in a manner that takes into account the overall structure of the data cloud. For p-variate data, p ≥ 2, there is an extant method for testing the hypothesis of a zero correlation for each pair of variables that is designed to control the probability of one or more Type I errors. And there are methods for the related situation where the focus is on the association between a dependent variable and p explanatory variables. However, there are limitations and several concerns with extant techniques. The paper describes alternative approaches that deal with these issues.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Rousselet, Dr Guillaume |
Authors: | Wilcox, R. R., Rousselet, G. A., and Pernet, C. R. |
College/School: | College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience |
Journal Name: | Journal of Statistical Computation and Simulation |
Publisher: | Taylor & Francis |
ISSN: | 0010-4817 |
ISSN (Online): | 1563-5163 |
Published Online: | 23 July 2018 |
Copyright Holders: | Copyright © 2018 Informa UK Limited, trading as Taylor and Francis Group |
First Published: | First published in Journal of Statistical Computation and Simulation 88(16): 3116-3131 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
Related URLs: | |
Data DOI: | 10.6084/m9.figshare.5768301.v2 |
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