Universal Consistency of Data-Driven Partitions for Divergence Estimation
This paper presents a general histogram based divergence estimator based on data-dependent partition. Sufficient conditions for the universal strong consistency of the data-driven divergence estimator, using Lugosi and Nobel's combinatorial notions for partition families, are presented. As a corollary this result is particularized for the emblematic case of l m-spacing quantization scheme.