Abstract Details
Activity Number:

90

Type:

Contributed

Date/Time:

Sunday, August 4, 2013 : 4:00 PM to 5:50 PM

Sponsor:

Section on Nonparametric Statistics

Abstract  #308350 
Title:

A Nonparametric Omnibus Independence Test Based on Copula Density

Author(s):

Gery Geenens*+

Companies:

UNSW

Keywords:

independence test ;
copula modelling ;
copula density ;
kernel density estimation ;
boundary bias ;
Cramervon Mises statistics

Abstract:

The concept of independence is central in statistics, and being able to test for the independence of two random variables X and Y is of course very important. We propose an independence test which is able to detect any departure from the null hypothesis of independence (omnibus) between two continuous random variables X and Y and which does not rely on any particular parametric assumptions on the distributions of X and Y (nonparametric). It is based on copula density estimation. Specifically, the test statistic is a Cramervon Misestype discrepancy measure between a (boundarybiascorrected) kernel estimate of the copula density of (X,Y) and the independence copula density. Basing an independence test on the copula density has numerous advantages that will be discussed during the talk, and the resulting test turns out to be very powerful at detecting subtle departures from independence in any direction. This is explained through theoretical considerations and illustrated by substantial simulation studies.

Authors who are presenting talks have a * after their name.
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