2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | E.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. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
