Knopov P.S., Kasitskaya E.J.
On large deviations of empirical estimates in a stochastic programming problem for a homogeneous random field with a discrete parameter

The problem of stochastic optimization is considered, where the random factor is a homogeneous, in a narrow sense, random field with a discrete parameter that satisfies the strong mixing condition. The primitive function of the criterion is replaced by an empirical one, based on observations of the field. According to the results of functional analysis and large deviations theory, large deviations of empirical estimates are investigated. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: homogeneous in a strict sense random field with a discrete parameter, large deviations principle, stochastic optimization problem, strong mixing condition, Mixing, Parameter estimation, Stochastic systems, Discrete parameters, Empirical estimate, Homogeneous in a strict sense random field with a discrete parameter, Large deviation principle, Large deviations, Programming problem, Random fields, Stochastic optimization problems, Stochastic optimizations, Strong mixing conditions, Stochastic programming

Cite:
Knopov P.S., Kasitskaya E.J. (2021). On large deviations of empirical estimates in a stochastic programming problem for a homogeneous random field with a discrete parameter. Cybernetics and Systems Analysis, 57 (5), 43–53. doi: https://doi.org/10.1007/s10559-021-00396-0 http://jnas.nbuv.gov.ua/article/UJRN-0001268747 [In Ukrainian].