Empirical Estimates in Stochastic Optimization and Identification

This book contains problems of stochastic optimization and identification. Results concerning uniform law of large numbers, convergence of approximate estimates of extreme points, as well as empirical estimates of functionals with probability 1 and in probability are presented.
Audience: Specialists in stochastic optimization and estimations, postgraduate students, and graduate students studying such topics

List of contents

1 Introduction.- 2 Parametric Empirical Methods.- 3 Parametric Regression Models.- 4 Periodogram Estimates for Random Processes and Fields.- 5 Nonparametric Identification Problems.- References.

Summary

This book contains problems of stochastic optimization and identification. Results concerning uniform law of large numbers, convergence of approximate estimates of extreme points, as well as empirical estimates of functionals with probability 1 and in probability are presented.

Audience: Specialists in stochastic optimization and estimations, postgraduate students, and graduate students studying such topics

Product details

• Authors: Evgeniya J Kasitskaya, Evgeniya J. Kasitskaya, Pavel Knopov, Pavel S Knopov, Pavel S. Knopov
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 26.06.2009
• EAN: 9781402007071
• ISBN: 978-1-4020-0707-1
• No. of pages: 250
• Weight: 549 g
• Illustrations: VIII, 250 p.
• Series: Applied Optimization; Applied Optimization
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; C, Optimization, Statistics, Mathematics and Statistics, Probability Theory and Stochastic Processes, Systems Theory, Control, Probability & statistics, Probabilities, Stochastics, Probability Theory, Mathematical optimization, Statistics, general, System Theory, Cybernetics & systems theory, Parameter;law of large numbers;optimization;probability