COMPUTATIONAL
STATISTICS
& DATA ANALYSIS
ELSEVIER Computational Statistics & Data Analysis 20 (1995) 643-656
Refined approximations to permutation
tests for multivariate inference
Frrdrrique Kazi-Aoual a'*, Simon Hitier b, Robert Sabatier c,
Jean-Dominique Lebreton 2
a Unitd de Biom~trie, INRA/ENSA.M/UM II, 9 Place Viala, 34060 Montpellier cedex 1, France
b Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, BP 5051, 34 033 Montpellier cedex 1, France
cLaboratoire de Physique Mol~culaire et Structurale, Facultb de Pharmacie, Universit~ Montpellier L 34 060
Montpellier cedex 1, France
Received 1 April 1994; Revised 1 September 1994
Abstract Various authors have proposed approximations to permutation tests of independence
between two data tables. We develop approximations based on explicit expressions of the first three
moments of three different test statistics under the permutation distribution. The rejection level is then
determined by using a Pearson-type III distribution matching the values of the first three moments.
We present three examples in which the relative merits of the test statistics are examined and the
results of the approximation procedure are compared with explicit permutation tests.
Keywords: Permutation tests; Exact permutation tests; Monte-Carlo methods; Multivariate infer-
ence; Randomization tests
1. Introduction
Multivariate inference procedures, such as the one-way multivariate analysis of
variance (MANOVA1; e.g., Krishnaiah, 1980), have been developed under the
assumption of Gaussian distributions (e.g., Giri, 1977). This framework is fre-
quently restrictive in biological applications. To avoid it, many authors have used
explicit permutation tests, made possible by the availability of fast computers (see
reviews by Edington, 1987; Manly, 1991). In this approach, to test for the indepen-
dence of a table Y and a table X, one gets the observed distribution of a test statistic
* Corresponding author.
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