Fourier methods for testing multivariate independence

Authors:
Simos G. Meintanis;George Iliopoulos
Affiliations:
Department of Economics, National and Kapodistrian University of Athens, 8 Pesmazoglou str., 10559 Athens, Greece;Department of Statistics and Insurance Science, University of Piraeus, 80 Karaoli & Dimitriou str., 18534 Piraeus, Greece
Venue:
Computational Statistics & Data Analysis
Year:
2008

Citing 4
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Abstract

Recently a power study of some popular tests for bivariate independence based on ranks has been conducted. An alternative class of tests appropriate for testing not only bivariate, but also multivariate independence is developed, and their small-sample performance is studied. The test statistics employ the familiar equation between the joint characteristic function and the product of component characteristic functions, and may be written in a closed form convenient for computer implementation. Simulations on a distribution-free version of the new test statistic show that the proposed method compares well to standard methods of testing independence via the empirical distribution function. The methods are applied to multivariate observations incorporating data from several major stock-market indices. Issues pertaining to the theoretical properties of the new test are also addressed.