We derive independence tests by means of dependence measures thresholding in a semiparametric context. Precisely, estimates of mutual information associated to ϕ-divergences are derived through the dual representations of ϕ-divergences. The asymptotic properties of the estimates are established, including consistency, asymptotic distribution and large deviations principle. The related tests of independence are compared through their relative asymptotic Bahadur efficiency and numerical simulations.
CITATION STYLE
Keziou, A., & Regnault, P. (2015). Generalized mutual-information based independence tests. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9389, pp. 454–463). Springer Verlag. https://doi.org/10.1007/978-3-319-25040-3_49
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