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A class of nonparametric estimators of the main functional of distribution constructed by making use auxiliary information is proposed. It is shown that the knowledge usage of other distribution functionals in estimation of the main functional can often provide the mean squared error (MSE) smaller than that of estimators constructed without such auxiliary information. In the paper, the adaptive estimators...
The paper deals with the control algorithms for discrete delayed systems with unknown inputs (disturbances) and model parameters. The control algorithm is based on the local criterion with using Kalman filters and nonparametric estimators. The example is given to illustrate the proposed approach.
The paper deals with the control algorithms for discrete delayed systems with unknown inputs (disturbances). Control algorithm is based on local criterion with using Kalman filtering and nonparametric estimator. Examples are given to illustrate the proposed approach.
Algorithm for inventory control with incomplete information about the model of demand is proposed. Algorithm is synthesized taking into account time delay. Inventory control algorithm based on local criterion with using Kalman filtering for systems with unknown input is constructed. Examples are given to illustrate the usefulness of the proposed approach.
The paper deals with the problem of estimating the actuarial present value of the continuous whole life and n-year term life annuities. We synthesize nonparametric estimators of these statuses of life annuity. The main parts of their asymptotic mean square errors for these estimators and their limit distributions are found. By individuals' death moments, both parametric and nonparametric estimates...
To identify an unknown function defining of a nonlinear ARX-process, we use kernel regression estimators. The principal parts of mean square errors for these estimators are found. The proposed algorithms are applied to the real data processing.
The paper deals with the Kalman filtering and control algorithms for a class of systems with uncertainty (unknown additive inputs). Such classes include object models with possible failures and also models of controlled processes with unknown disturbances and parameters. The designed algorithms are based on combining the Kalman filter and nonparametric estimator. Examples are given to illustrate the...
The paper addressed the filtering and prediction problems with using nonparametric algorithms for discrete stochastic systems with unknown input. The designed algorithms are based on combining the Kalman filter and nonparametric estimator. The optimal properties of the explored algorithms are proved. Examples are given to illustrate the usefulness of the proposed approach.
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