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Application of a nonparametric pattern recognition algorithm in the problem of testing the hypothesis about the independence of variables of ambiguous functions
https://doi.org/10.32446/0368-1025it.2022-01-17-22
Abstract
The problem of testing the hypothesis about the independence of two-dimensional random variables in the analysis of variable ambiguous functions is considered. To solve it, a technique is used based on a nonparametric pattern recognition algorithm of the kernel type corresponding to the maximum likelihood criterion. The use of this technique makes it possible to bypass the problem of decomposition of the range of values of random variables into intervals. Based on the results of computational experiments, the eff ectiveness of the applied technique was evaluated depending on the type of ambiguous functions, the level of random noise and the volume of initial statistical data. The results obtained are relevant for solving the problem of detecting natural and technical objects using remote sensing data.
About the Authors
A. V. Lapko
Institute of Computational Modelling, Siberian Branch of the Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Russian Federation
Russian Federation
Aleksandr V. Lapko
Krasnoyarsk
V. A. Lapko
Institute of Computational Modelling, Siberian Branch of the Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Russian Federation
Russian Federation
Vasiliy A. Lapko
Krasnoyarsk
A. V. Bakhtina
Reshetnev Siberian State University of Science and Technology
Russian Federation
Russian Federation
Anna V. Bakhtina
Krasnoyarsk
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Review
For citations:
Lapko A.V., Lapko V.A., Bakhtina A.V. Application of a nonparametric pattern recognition algorithm in the problem of testing the hypothesis about the independence of variables of ambiguous functions. Izmeritel`naya Tekhnika. 2022;(1):17-22. (In Russ.) https://doi.org/10.32446/0368-1025it.2022-01-17-22
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