Economics at your fingertips  

Multivariate nonparametric test of independence

Yanan Fan, Pierre Lafaye de Micheaux, Spiridon Penev and Donna Salopek

Journal of Multivariate Analysis, 2017, vol. 153, issue C, 189-210

Abstract: The problem of testing mutual independence of p random vectors in a general setting where the dimensions of the vectors can be different and the distributions can be discrete, continuous or both is of great importance. We propose such a test which utilizes multivariate characteristic functions and is a generalization of known results. We characterize the limiting distribution of the test statistic under the null hypothesis. The limiting null distribution is approximated and the method is validated. Numerical results based on simulations are investigated and our methodology is implemented in the R package IndependenceTests. Power comparisons are also presented for some partial cases of our general test, where some competitive procedures exist.

Keywords: Central limit theorem; Empirical characteristic function; Multivariate K sample independence (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2016.09.014

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-05-02
Handle: RePEc:eee:jmvana:v:153:y:2017:i:c:p:189-210