Тип публикации: статья из журнала

Год издания: 2021

Идентификатор DOI: 10.18287/2412-6179-CO-871

Ключевые слова: bandwidths of kernel functions, kolmogorov–smirnov criterion, multidimensional random variables, nonparametric probability density estimation, pattern recognition, spectral analysis of remote sensing data, testing a hypothesis of the independence of random variables, kolmogorov-smirnov criterion

Аннотация: A new method for testing a hypothesis of the independence of multidimensional random variables is proposed. The technique under consideration is based on the use of a nonparametric pattern recognition algorithm that meets a maximum likelihood criterion. In contrast to the traditional formulation of the pattern recognition problem, Показать полностьюthere is no a priori training sample. The initial information is represented by statistical data, which are made up of the values of a multivariate random variable. The distribution laws of random variables in the classes are estimated according to the initial statistical data for the conditions of their dependence and independence. When selecting optimal bandwidths for nonparametric kernel-type probability density estimates, the minimum standard deviation is used as a criterion. Estimates of the probability of pattern recognition error in the classes are calculated. Based on the minimum value of the estimates of the probabilities of pattern recognition errors, a decision is made on the independence or dependence of the random vari-ables. The technique developed is used in the spectral analysis of remote sensing data. © 2021, Institution of Russian Academy of Sciences. All rights reserved. A new method for testing a hypothesis of the independence of multidimensional random variables is proposed. The technique under consideration is based on the use of a nonparametric pattern recognition algorithm that meets a maximum likelihood criterion. In contrast to the traditional formulation of the pattern recognition problem, there is no a priori training sample. The initial information is represented by statistical data, which are made up of the values of a multivariate random variable. The distribution laws of random variables in the classes are estimated according to the initial statistical data for the conditions of their dependence and independence. When selecting optimal bandwidths for nonparametric kernel-type probability density estimates, the minimum standard deviation is used as a criterion. Estimates of the probability of pattern recognition error in the classes are calculated. Based on the minimum value of the estimates of the probabilities of pattern recognition errors, a decision is made on the independence or dependence of the random variables. The technique developed is used in the spectral analysis of remote sensing data.

Журнал: Computer Optics

Выпуск журнала: Vol. 45, Is. 5

Номера страниц: 767-772

ISSN журнала: 01342452

Издатель: Institution of Russian Academy of Sciences

• Zenkov I.V. (Siberian Federal University, Svobodny Av. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
• Lapko A.V. (Institute of Computational Modelling SB RAS, Akademgorodok 50, Krasnoyarsk, 660036, Russian Federation, Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
• Lapko V.A. (Institute of Computational Modelling SB RAS, Akademgorodok 50, Krasnoyarsk, 660036, Russian Federation, Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
• Kiryushina E.V. (Siberian Federal University, Svobodny Av. 79, Krasnoyarsk, 660041, Russian Federation)
• Vokin V.N. (Siberian Federal University, Svobodny Av. 79, Krasnoyarsk, 660041, Russian Federation)

• Scopus
• Web of Science Core Collection
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