Department or Administrative Unit
Computer Science
Document Type
Article
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Author Copyright
Copyright © 2006-2017 by CCC Publications
Publication Date
8-2017
Journal
International Journal of Computers Communications & Control
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.
Recommended Citation
CAȚARON, Angel; ANDONIE, Razvan; CHUEH, Yvonne. Asymptotically Unbiased Estimation of A Nonsymmetric Dependence Measure Applied to Sensor Data Analytics and Financial Time Series. International Journal of Computers Communications & Control, [S.l.], v. 12, n. 4, p. 475-491, june 2017. ISSN 1841-9844. doi: https://doi.org/10.15837/ijccc.2017.4.2928.
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.