A multivariate empirical characteristic function test of independence with normal marginals

• Published in: Journal of multivariate analysis Vol. 95; no. 2; pp. 345 - 369
• Main Authors: Bilodeau, M., Lafaye de Micheaux, P.
• Format: Journal Article
• Language: English
• Published: San Diego, CA Elsevier Inc 2005; Elsevier; Taylor & Francis LLC
• Series: Journal of Multivariate Analysis
• Subjects: Characteristic function; Characteristic function Independence Multivariate analysis Serial independence Stochastic processes; Convergence; Distribution theory; Exact sciences and technology; Hypothesis testing; Independence; Inference from stochastic processes; time series analysis; Limit theorems; Mathematics; Multivariate analysis; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Serial independence; Statistics; Stochastic processes; Studies; 62H15; Characteristic function; 62M99; Serial independence; Stochastic processes; 62E20; Independence; Multivariate analysis; 60F05; Empirical process; Gaussian distribution; Eigenvalue; Stochastic process; Von Mises statistic; Marginal distribution; Distribution function; Random effect; 60F05 Characteristic function; Test statistic; 1991 subject classification: 62H15: 62M99; Empirical distribution function; Cornish Fisher expansion; Cramer von Mises test; Weak convergence; Statistical method; N variable function; Independence test; Parametric test

This paper proposes a semi-parametric test of independence (or serial independence) between marginal vectors each of which is normally distributed but without assuming the joint normality of these marginal vectors. The test statistic is a Cramér–von Mises functional of a process defined from the emp...