title: An affine invariant rank-based method for comparing dependent groups.
abstract: A basic property of various rank-based hypothesis testing methods is that they are invariant under a linear transformation of the data. For multivariate data, a generalization of this property is sometimes sought (called affine invariance), but typically techniques for assigning ranks do not achieve this goal, or it is assumed that sampling is from a symmetric distribution. A rank-based method is suggested for comparing dependent groups that is based on halfspace depth, is affine invariant in terms of difference scores, and allows sampling from asymmetric distributions.
authors.affiliation: Department of Psychology, University of Southern California, Los Angeles 90089-1061, USA. rwilcox@usc.edu
authors.forename: Rand R
authors.initials: RR
authors.lastname: Wilcox
mesh_terms: D000704:Analysis of Variance; D015982:Bias; D006801:Humans; D016014:Linear Models; D011594:Psychometrics / Q000706:statistics & numerical data*; D016008:Statistical Distributions; D018709:Statistics, Nonparametric