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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Vydané v:	Optoelectronics, instrumentation, and data processing Ročník 59; číslo 5; s. 551 - 560
Hlavní autori:	Lapko, A. V., Lapko, V. A., Bakhtina, A. V.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Moscow Pleiades Publishing 01.10.2023
Predmet:	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
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