Empirical Estimates in Stochastic Optimization and Identification
By: by Pavel S. Knopov, Evgeniya J. Kasitskaya
Contributors: Kasitskaya, Evgeniya J.
Series: Applied Optimization -- 71; Applied optimization -- 71.
Publisher: Springer US
Published: 2002
Place of publication: Boston, MA
Language: eng
Format: [electronic resource] /
Number of pages: 250
ISBN-13: 9781441952240; 9781475735673
Subjects: Mathematical optimization; Statistics; Distribution (Probability theory); Systems theory
Description: This book contains problems of stochastic optimization and identification. Results concerning uniform law of large numbers, convergence of approximate estimates of extremal points, as well as empirical estimates of functionals with probability 1 and in probability are presented. It is shown that the investigation of asymptotic properties of approximate estimates and estimates of unknown parameters in various regression models can be carried out by using general methods, which are presented by the authors. The connection between stochastic programming methods and estimation theory is described. It was assumed to use the methods of asymptotic stochastic analysis for investigation of extremal points, and on the other hand to use stochastic programming methods to find optimal estimates. Audience: Specialists in stochastic optimization and estimations, postgraduate students, and graduate students studying such topics.
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