О ВОЗМОЖНОСТИ ПОВЫШЕНИЯ СТАТИСТИЧЕСКОЙ ЗНАЧИМОСТИ РЕГРЕССИОННЫХ ЗАВИСИМОСТЕЙ В ЗАДАЧАХ ОБРАБОТКИ ЭКСПЕРИМЕНТАЛЬНЫХ ВЫБОРОК МАЛОГО ОБЪЕМА

Abstract

Listen

Translate article

The article considers the problem of increasing the reliability and statistical significance of regression models of research objects built on small experimental data samples. Insufficient amount of experimental data forces the researcher to use linear models with a minimum number of variable factors, however, even with such a choice of the type of model, insufficient statistical significance of parameter estimates excludes the possibility of using it for reliable forecasting of changes in the explained variables. In order to expand the possibility of choosing the type of model at the specification stage and to increase the statistical significance of its parameter estimates, it is proposed to expand the volume of experimental data using a statistical model of the object of study, built on the basis of a generative adversarial neural network. When training on a small sample obtained during an experimental study of the object, the generator of a conditional generative adversarial network generates data clusters with centroids corresponding to the points of the training (experimental) sample. The results of the analysis of the data of a physical experiment are presented, confirming its main provisions.