Application of a Nonparametric Pattern Recognition Algorithm to the Problem of Testing the Hypothesis of the Independence of Variables of Multi-Valued Functions

The problem of hypothesis testing for the independence of two-dimensional random variables in the analysis of variables of multi-valued functions is considered. To solve it, we used a technique based on a nonparametric kernel-type pattern recognition algorithm corresponding to the maximum likelihood...

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Veröffentlicht in:	Measurement techniques Jg. 65; H. 1; S. 17 - 23
Hauptverfasser:	Lapko, A. V., Lapko, V. A., Bakhtina, A. V.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2022 Springer Springer Nature B.V
Schlagworte:	Algorithms Analytical Chemistry Characterization and Evaluation of Materials Hypotheses Measurement Science and Instrumentation Nonparametric statistics Object recognition Pattern recognition Physical Chemistry Physics Physics and Astronomy Random noise Random variables Remote sensing Two dimensional analysis maximum likelihood test kernel estimation of probability density hypothesis testing multi-valued functional dependencies dependent random variables two-dimensional random variables independent random variables pattern recognition algorithm
ISSN:	0543-1972, 1573-8906
Online-Zugang:	Volltext
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