Gololobov D.A.
Asymptotic properties of the method of empirical estimate for non-stationary random fields

The author considers a stochastic programming problem where the estimator is approximated by its empirical estimate based on observations of a non-homogeneous random field with continuous time and strong mixing. The strong consistency of this estimate is investigated and its asymptotic distribution is found under the constraint imposed on the unknown parameter in the form of systems of inequalities. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: continuous time, function, method of observed means, minimization, nonstationary field, probability, random field, Functions, Optimization, Probability, Stochastic programming, Stochastic systems, Asymptotic distributions, Asymptotic properties, Continuous-time, Empirical estimate, method of observed means, Nonstationary, Random fields, Strong consistency, Continuous time systems

Cite:
Gololobov D.A. (2018). Asymptotic properties of the method of empirical estimate for non-stationary random fields. Cybernetics and Systems Analysis, 54 (6), 189-192. doi: https://doi.org/10.1007/s10559-018-0105-1 http://jnas.nbuv.gov.ua/article/UJRN-0000926965 [In Russian].