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

Inhaltsverzeichnis

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

Zusammenfassung

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

Produktdetails

• Autoren: Evgeniya J Kasitskaya, Evgeniya J. Kasitskaya, Pavel Knopov, Pavel S Knopov, Pavel S. Knopov
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Taschenbuch
• Erschienen: 21.10.2010
• EAN: 9781441952240
• ISBN: 978-1-4419-5224-0
• Seiten: 250
• Gewicht: 454 g
• Illustration: VIII, 250 p.
• Serien: Applied Optimization; Applied Optimization
• Thema: Naturwissenschaften, Medizin, Informatik, Technik > Mathematik > Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik