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Paper IDE.6.4
Paper Title Data-Driven Representations for Testing Independence: A Connection with Mutual Information Estimation
Authors Mauricio Gonzales, Jorge F. Silva, Universidad de Chile, Chile
Session E.6: Hypothesis Testing II
Presentation Lecture
Track Detection and Estimation
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract From the design of a data-driven partition, this paper addresses the problem of testing independence between two multidimensional random variables from i.i.d. samples. The empirical log-likelihood statistics is adopted with the objective of approximating the sufficient statistics of a test against independence that knows the two distributions (the oracle test). It is shown that approximating the sufficient statistics of the oracle test (asymptotically) offers a connection with the problem of estimating mutual information. Applying these ideas in the context of a data-dependent tree-structured partition (TSP), we derive concrete sufficient conditions on the parameters of the TSP scheme to obtain a strongly consistent test of independence distribution-free over the family of joint probabilities equipped with densities.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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