Improved methods for making inferences about multiple skipped correlations

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
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 > Institute of Neuroscience and Psychology
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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