The accuracy of modeling of Gaussian stochastic process in some Orlicz spaces

  • YU. V. Kozachenko Department of Probability Theory, Statistics and Actuarial Mathematics, Taras Shevchenko National University of Kyiv, Ukraine
  • Antonina Tegza Department of Probability Theory and Mathematical Analysis, Uzhhorod National University, Ukraine
  • N. V.Troshki Department of Physical and Mathematical Disciplines, Ukrainian-Hungarian Educational Institute, Ukraine
DOI: https://doi.org/10.19139/soic-2310-5070-670
Keywords: Sub-Gaussian process, stochastic process, model of the process, Orlicz space, the accuracy and reliability.

Abstract

The main purpose of this study is the construction of a model of a Gaussian stochastic process with given reliability and accuracy in some Orlicz spaces. In the paper, a suitable model is presented, conditions for the model parameters are derived, and some examples of their calculations are given.

Author Biographies

YU. V. Kozachenko, Department of Probability Theory, Statistics and Actuarial Mathematics, Taras Shevchenko National University of Kyiv, Ukraine
Department of Probability Theory, Statistics and Actuarial Mathematics, Taras Shevchenko National University of Kyiv, Ukraine Faculty of Mathematics and Information Technology, Vasyl’ Stus Donetsk National University, Ukraine
Antonina Tegza, Department of Probability Theory and Mathematical Analysis, Uzhhorod National University, Ukraine
Department of Probability Theory and Mathematical Analysis, Uzhhorod National University, Ukraine
N. V.Troshki, Department of Physical and Mathematical Disciplines, Ukrainian-Hungarian Educational Institute, Ukraine
Department of Physical and Mathematical Disciplines, Ukrainian-Hungarian Educational Institute, Ukraine

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Published
2020-02-17
How to Cite
V. Kozachenko, Y., Tegza, A., & V.Troshki, N. (2020). The accuracy of modeling of Gaussian stochastic process in some Orlicz spaces. Statistics, Optimization & Information Computing, 8(1), 127-135. https://doi.org/10.19139/soic-2310-5070-670
Issue
Vol 8 No 1 (2020)
Section
Research Articles
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