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Abstract:
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We introduce a new class of measures for testing independence between two random vectors, which uses expected difference of conditional and marginal characteristic functions. By choosing a particular weight function in the class, we propose a new index for measuring independence and study its property. To illustrate the use of such an index, two empirical versions are developed: slicing and kernel approaches. Their properties, asymptotics, connection with existing measures and applications are discussed. Implementation and Monte Carlo results are also presented.
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