Comparison of Methods for Testing the Hypothesis of Independence of Random Variables Based on a Nonparametric Classifier and Pearson’s Chi-Squared Test

A technique for testing the hypothesis about the independence of random variables, based on a nonparametric pattern recognition algorithm, is used in the analysis of ambiguous dependencies. The pattern recognition algorithm meets the maximum likelihood criterion. The assessment of distribution laws...

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Bibliographic Details

Published in:	Optoelectronics, instrumentation, and data processing Vol. 59; no. 5; pp. 551 - 560
Main Authors:	Lapko, A. V., Lapko, V. A., Bakhtina, A. V.
Format:	Journal Article
Language:	English
Published:	Moscow Pleiades Publishing 01.10.2023
Subjects:	Analysis and Synthesis of Signals and Images Lasers Optical Devices Optics Photonics Physics Physics and Astronomy ambiguous functional dependences kernel probability density estimate testing the hypothesis of independence of random variables two-dimensional random variables nonparametric pattern recognition algorithm Pearson’s chi-squared test
ISSN:	8756-6990, 1934-7944
Online Access:	Get full text
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