Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

8-2017

Abstract

A fundamental concept frequently applied to statistical machine learning is the detection of dependencies between unknown random variables found from data samples. In previous work, we have introduced a nonparametric unilateral dependence measure based on Onicescu’s information energy and a kNN method for estimating this measure from an available sample set of discrete or continuous variables. This paper provides the formal proofs which show that the estimator is asymptotically unbiased and has asymptotic zero variance when the sample size increases. It implies that the estimator has good statistical qualities. We investigate the performance of the estimator for data analysis applications in sensor data analysis and financial time series.

Comments

This article was originally published Open Access in International Journal of Computers Communications & Control. The full-text article from the publisher can be found here.

Journal

International Journal of Computers Communications & Control

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Rights

Copyright © 2006-2017 by CCC Publications

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