Empirical Estimates in Stochastic Optimization and Identification
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Author
Contributions
- Kasitskaya, Evgeniya J. - Contributor
Publication
2002 - Springer US, Boston, MA, United States
Language
English
Word Count
62,500 words, Guess
Page Count
250 pages
Physical Format
[electronic resource] /
Identifiers
- Open LibraryOL27035960M
- ISBN-139781441952240
- ISBN-101441952241
- OCLC Control Number851784174
- OCLC Control Numberempiricalestimat00knop
Classifications
- DDC519.5
- LCCQA276-280
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.
Subjects
Series Statement
- Applied Optimization -- 71
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