A CONDITIONAL DISTRIBUTION FUNCTION BASED APPROACH TO DESIGN NONPARAMETRIC TESTS OF INDEPENDENCE AND CONDITIONAL INDEPENDENCE

Measures of independence and conditional independence are two important statistical concepts that have found profound applications in engineering such as in feature selection and causality detection, respectively. Therefore, designing efficient ways, typically nonparametric, to estimate these measures has been an active research area in the last decade. In this paper, we propose a novel framework to test (conditional) independence, using the concept of conditional distribution function. Although, estimating conditional distribution function is a difficult task on its own, we show that the proposed measures can be estimated efficiently and actually can be expressed as the Frobenius norm of a matrix. We compare the proposed methods with other state-of-the-art techniques and show that they yield very promising results.