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...

Celý popis

Podrobná bibliografie

Vydáno v:	Optoelectronics, instrumentation, and data processing Ročník 59; číslo 5; s. 551 - 560
Hlavní autoři:	Lapko, A. V., Lapko, V. A., Bakhtina, A. V.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Moscow Pleiades Publishing 01.10.2023
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!