Test of bivariate independence based on angular probability integral transform with emphasis on circular-circular and circular-linear data

The probability integral transform of a continuous random variable with distribution function is a uniformly distributed random variable . We define the angular probability integral transform (APIT) as , which corresponds to a uniformly distributed angle on the unit circle. For circular (angular) ra...

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Bibliographic Details
Published in:Dependence modeling Vol. 11; no. 1; pp. 547 - 556
Main Authors: Fernández-Durán, Juan José, Gregorio-Domínguez, María Mercedes
Format: Journal Article
Language:English
Published: De Gruyter 20.10.2023
Subjects:
62H05
62H11
circular uniformity tests
circular-circular dependence
circular-linear dependence
copula
dependence measures
ISSN:2300-2298, 2300-2298
Online Access:Get full text
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Summary:The probability integral transform of a continuous random variable with distribution function is a uniformly distributed random variable . We define the angular probability integral transform (APIT) as , which corresponds to a uniformly distributed angle on the unit circle. For circular (angular) random variables, the sum modulus 2 of absolutely continuous independent circular uniform random variables is a circular uniform random variable, that is, the circular uniform distribution is closed under summation modulus 2 , and it is a stable continuous distribution on the unit circle. If we consider the sum (difference) of the APITs of two random variables, and , and test for the circular uniformity of their sum (difference) modulus 2 , this is equivalent to test of independence of the original variables. In this study, we used a flexible family of nonnegative trigonometric sums (NNTS) circular distributions, which include the uniform circular distribution as a member of the family, to evaluate the power of the proposed independence test by generating samples from NNTS alternative distributions that could be at a closer proximity with respect to the circular uniform null distribution.
ISSN:2300-2298
2300-2298
DOI:10.1515/demo-2023-0103