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Combined identification algorithms Y. G. Dmitriev, G. M. Koshkin, V. Y. Lukov

By: Dmitriev, Yury GContributor(s): Koshkin, Gennady M | Lukov, Vadim YuMaterial type: ArticleArticleSubject(s): комбинированные алгоритмы | идентификация | прогнозирование | комбинированные оценкиGenre/Form: статьи в сборниках Online resources: Click here to access online In: Applied methods of statistical analysis. Nonparametric methods in cybernetics and system analysis - AMSA'2017, Krasnoyarsk, Russia, 18-22 September, 2017 : proceedings of the international workshop P. 19-27Abstract: In many applied problems it is required to construct a mathematical model of the dependence of output variables on input variables of the stochastic object. To solve this problem, both parametric and nonparametric approaches are used. Each of these approaches has advantages and disadvantages. In the paper, we consider combined algorithms for the identifcation of stochastic objects using jointly nonparametric and parametric estimates of regression.
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Библиогр.: 11 назв.

In many applied problems it is required to construct a mathematical model
of the dependence of output variables on input variables of the stochastic object.
To solve this problem, both parametric and nonparametric approaches are used.
Each of these approaches has advantages and disadvantages. In the paper, we
consider combined algorithms for the identifcation of stochastic objects using
jointly nonparametric and parametric estimates of regression.

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