1. R 50.1.037-2002. Recommendations for standardization. Applied statistics. Rules of check of experimental and theoretical distribution of the consent. Part I. Chi-square criteria. Moscow: Gosstandart Rossii; 2001. (in Russ.)
2. Ivanov A.I., Kupriyanov E.N., Tureev S.V. Neural network integration of classical statistical tests for processing small samples of biometrics data. Dependability 2019;2:22-27. DOI: 10.21683/1729-2646-2019-19-2-22-27.
3. Akhmetov B.B., Ivanov A.I. Estimation of quality of a small sampling biometric data using a more efficient form of the chi-square test. Dependability 2016;16(2):43-48.
4. Volchikhin V.I., Ivanov A.I., Bezyaev A.V., Kupriyanov E.N. The Neural Network Analysis of Normality of Small Samples of Biometric Data through Using the Chi-Square Test and Anderson–Darling Criteria. Engineering Technologies and Systems. 2019;29(2):205-217. DOI: 10.15507/2658-4123.029/2019.02.205-217.
5. Ivanov A.I., Bannych A.G., Kupriyanov E.N. et al. Collection of artificial neuron equivalent statistical criteria for their use when testing the hypothesis of normality of small samples of biometric data. Proceedings of the I All-Russian Science and Technology Conference Security of Information Technology. Penza. 2019. 156-164.