On the formation of norms based on regression models for small samples
https://doi.org/10.22405/2226-8383-2025-26-1-149-156
Abstract
The extension of the method of forming norms for structural elements of a complex system based on the methodology of Bayesian intelligent measurements and econometric modeling in small samples is presented. The implementation of the method is demonstrated using the example of agriculture in the Tula region.
About the Author
Roman Aleksandrovich ZhukovRussian Federation
doctor of economic sciences, candidate of physical and mathematical sciences
References
1. Matushkina, I. Y., Onishchenko L. A., 2018, “Technical regulation: technical regulations and standardization: a textbook“, Yekaterinburg, Ural University Publishing House, 208 p.
2. Madera, A. G. 2017, “Modeling and optimization of business processes and process systems under conditions of uncertainty“, Business Informatics, no. 4, pp. 74–82.
3. Trung, D. D. 2022, “Development of data normalization methods for multi-criteria decision making: Applying for MARCOS method“, Manufacturing Review, vol. 9, no. 4, pp. 15.
4. Zhukov, R. A. 2021, “Method for assessing the results of hierarchical socio-economic systems’ functioning based on the aggregated production function“, Economics and Mathematical Methods, vol. 57, no. 3, pp. 17-31.
5. Ayvazyan, S. A. 2008, “Bayesian approach in econometric analysis“, Applied Econometrics, no. 1 (9), pp. 93-130.
6. Prokopchina, S. V. 2020, “Bayesian intelligent technologies in problems of modeling the distribution law under uncertainty: monograph“, M., Publishing house “SCIENTIFIC LIBRARY“, 292 p.
7. Prokopchina, S. V. 2021, “Fundamentals of the theory of scaling in economics: textbook“, M., Publishing house “SCIENTIFIC LIBRARY“, 272 p.
8. Prokopchina, S. V. 2021, “Bayesian intellectual measurements“, M., Publishing house “SCIENTIFIC LIBRARY“, 495 p.
9. Zhukov, R. A., Prokopchina, S. V. 2024, “On the formation of soft norms for evaluating the functioning of complex systems“, Chebyshevskii Sbornik, vol. 25, no. 3 (94), pp. 351-358.
10. Zhukov, R. A., Prokopchina, S. V., Plinskaya, M. A., Zhelunitsina, M. A. 2024, “Building a system of dynamic norms for evaluating the functioning of complex systems on the example of the regions of the Central Federal District“, Business Informatics, vol. 18, no. 4, pp. 46-60.
11. Prokopchina, S. V., Shcherbakov, G. A., Efimov, Yu. V. 2019, “Modeling of socio-economic systems in conditions of uncertainty“, M., Publishing house “SCIENTIFIC LIBRARY“, 508 p.
12. Zhukov, R. A., Prokopchina, S. V., Plinskaya, M. A., Zhelunitsina, M. A. 2024, ‘Modeling of functional relationships of regional economic systems based on small samples based on Bayesian intelligent measurements“, Journal of Applied Economic Research, vol. 23, no. 3, pp. 721-750.
13. Federal State Statistics Service of the Russian Federation. URL: https://gks.ru.
14. Tables of inflation rates. URL: https://xn—-ctbjnaatncev9av3a8f8b.xn–p1ai/
15. Zhukov R.A., Prokopchina S.V. The Infoanalyst 2.0 software package. Certificate of state registration of the computer program No. 2024617544 dated 04/03/2024. URL: https://elibrary.ru/item.asp?id=65627372
16. Zhukov, R. A., Prokopchina, S. V., Giniatov, I. A., Nikolina, E. M. 2022, “Application of the Bayesian Mathematical Statistics Library in the Infointegrator Software Package“, Soft Measurement and Computing, vol. 54, no. 5, pp. 99–108.
17. Zhukov R. A. 2020, “An approach to assessing the functioning of hierarchical socio-economic systems and decision-making based on the EFRA software package“, Business Informatics, vol. 14, no. 3, pp. 82-95.
Review
For citations:
Zhukov R.A. On the formation of norms based on regression models for small samples. Chebyshevskii Sbornik. 2025;26(1):149-156. (In Russ.) https://doi.org/10.22405/2226-8383-2025-26-1-149-156