A posteriori minimax estimation with likelihood constraintsстатья
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Дата последнего поиска статьи во внешних источниках: 10 октября 2019 г.
Автор:
Borisov A.V.
Журнал:
Automation and Remote Control
Том:
73
Номер:
9
Год издания:
2012
Издательство:
Pleiades Publishing, Ltd
Местоположение издательства:
Road Town, United Kingdom
Первая страница:
1481
Последняя страница:
1497
DOI:
10.1134/S0005117912090044
Аннотация:
Consideration was given to the problem of guaranteed estimation of the parameters of an uncertain stochastic regression. The loss function is the conditional mean squared error relative to the available observations. The uncertainty set is a subset of the probabilistic distributions lumped on a certain compact with additional linear constraints generated by the likelihood function. Solution of this estimation problem comes to determining the saddle point defining both the minimax estimator and the set of the corresponding worst distributions. The saddle point is the solution of a simpler finite-dimensional dual optimization problem. A numerical algorithm to solve this problem was presented, and its precision was determined. Model examples demonstrated the impact of the additional likelihood constraints on the estimation performance. В© Pleiades Publishing, Ltd., 2012.
Добавил в систему:
Борисов Андрей Владимирович