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Testing for positive quadrant dependence.


ABSTRACT: We develop an empirical likelihood approach to test independence of two univariate random variables X and Y versus the alternative that X and Y are strictly positive quadrant dependent (PQD). Establishing this type of ordering between X and Y is of interest in many applications, including finance, insurance, engineering, and other areas. Adopting the framework in Einmahl and McKeague (2003, Bernoulli), we create a distribution-free test statistic that integrates a localized empirical likelihood ratio test statistic with respect to the empirical joint distribution of X and Y. When compared to well known existing tests and distance-based tests we develop by using copula functions, simulation results show the EL testing procedure performs well in a variety of scenarios when X and Y are strictly PQD. We use three data sets for illustration and provide an online R resource practitioners can use to implement the methods in this article.

SUBMITTER: Tang CF 

PROVIDER: S-EPMC7597655 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Testing for positive quadrant dependence.

Tang Chuan-Fa CF   Wang Dewei D   El Barmi Hammou H   Tebbs Joshua M JM  

The American statistician 20190530


We develop an empirical likelihood approach to test independence of two univariate random variables <i>X</i> and <i>Y</i> versus the alternative that <i>X</i> and <i>Y</i> are strictly positive quadrant dependent (PQD). Establishing this type of ordering between <i>X</i> and <i>Y</i> is of interest in many applications, including finance, insurance, engineering, and other areas. Adopting the framework in Einmahl and McKeague (2003, <i>Bernoulli</i>), we create a distribution-free test statistic  ...[more]

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