Mathematical model for identifying the leading geopolitical actor by the principal component analysis

Yu. A. Pichugin, O. A. Malafeyev, I. V. Zaitseva, A. I. Voskoboev, V. V. Shabaev

Результат исследований: Научные публикации в периодических изданияхстатья в журнале по материалам конференции

Выдержка

In this paper, an approach is developed that allows one to solve an applied problem of identifying hidden factors of geopolitical influence. It uses the principal component analysis. The solution of the problem is based on the principal components and the method of informative selection of the components of the response of the linear regression model. A numerical example is given on the basis of data on the cost of armament of a number of leading countries.

Язык оригиналаанглийский
Номер статьи022093
ЖурналIOP Conference Series: Earth and Environmental Science
Том315
Номер выпуска2
DOI
СостояниеОпубликовано - 23 авг 2019
СобытиеInternational Scientific Conference on Agribusiness, Environmental Engineering and Biotechnologies, AGRITECH 2019 - Krasnoyarsk, Российская Федерация
Продолжительность: 20 июн 201922 июн 2019

Отпечаток

principal component analysis
cost
armament
method

Предметные области Scopus

  • Науки об окружающей среде (все)
  • Планетоведение и науки о земле (все)

Цитировать

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Mathematical model for identifying the leading geopolitical actor by the principal component analysis. / Pichugin, Yu. A.; Malafeyev, O. A.; Zaitseva, I. V.; Voskoboev, A. I.; Shabaev, V. V.

В: IOP Conference Series: Earth and Environmental Science , Том 315, № 2, 022093, 23.08.2019.

Результат исследований: Научные публикации в периодических изданияхстатья в журнале по материалам конференции

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T1 - Mathematical model for identifying the leading geopolitical actor by the principal component analysis

AU - Pichugin, Yu. A.

AU - Malafeyev, O. A.

AU - Zaitseva, I. V.

AU - Voskoboev, A. I.

AU - Shabaev, V. V.

PY - 2019/8/23

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KW - Biotechnology

KW - Environmental engineering

KW - regression analysis

KW - Linear regression models

KW - Principal Components

KW - principal component analysis

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