On sequential confidence estimation of parameters of stochastic dynamical systems with conditionally Gaussian noises

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On sequential confidence estimation of parameters of stochastic dynamical systems with conditionally Gaussian noises	460 KB	Adobe Acrobat PDF	Read	Download
DOI Доступ к ресурсу на сайте издателя			10.1134/S0005117917100058

Title: On sequential confidence estimation of parameters of stochastic dynamical systems with conditionally Gaussian noises
Creator: Vorobeychikov, Sergey E.
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Konev, Victor V.
Date: 2017
DOI: 10.1134/S0005117917100058
Description: We consider the problem of non-asymptotical confidence estimation of linear parameters in multidimensional dynamical systems defined by general regression models with discrete time and conditionally Gaussian noises under the assumption that the number of unknown parameters does not exceed the dimension of the observed process. We develop a non-asymptotical sequential procedure for constructing a confidence region for the vector of unknown parameters with a given diameter and given confidence coefficient that uses a special rule for stopping the observations. A key role in the procedure is played by a novel property established for sequential least squares point estimates earlier proposed by the authors. With a numerical modeling example of a two-dimensional first order autoregression process with random parameters, we illustrate the possibilities for applying confidence estimates to construct adaptive predictions.

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collection(s): Факультет прикладной математики и кибернетики (до 01.09.2017 г.)
Identifier: смотреть в электронном каталоге НБ ТГУ
Type: статьи в журналах
Source: Automation and remote control. 2017. Vol. 78, № 10. P. 1803-1818
Language: eng

Created: 26-01-2018

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Факультет прикладной математики и кибернетики (до 01.09.2017 г.) On sequential confidence estimation of parameters of stochastic dynamical systems with conditionally Gaussian noises