Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensive coverage of the various aspects of experimental design for nonlinear models. The book contains original contributions to the theory of optimal experiments that will interest students and researchers in the field. Practitionners motivated by applications will find valuable tools to help them designing their experiments.
The first three chapters expose the connections between the asymptotic properties of estimators in parametric models and experimental design, with more emphasis than usual on some particular aspects like the estimation of a nonlinear function of the model parameters, models with heteroscedastic errors, etc. Classical optimality criteria based on those asymptotic properties are then presented thoroughly in a special chapter.
Three chapters are dedicated to specific issues raised by nonlinear models. The construction of design criteria derived from non-asymptotic considerations (small-sample situation) is detailed. The connection between design and identifiability/estimability issues is investigated. Several approaches are presented to face the problem caused by the dependence of an optimal design on the value of the parameters to be estimated.
A survey of algorithmic methods for the construction of optimal designs is provided.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Luc Pronzato is Directeur de Recherche at CNRS (French National Center for Scientific Research). From 2008 to 2011 he directed the I3S Laboratory (Informatique, Signaux et Systèmes, Sophia-Antipolis), University of Nice-Sophia-Antipolis/CNRS, where he is still working. He his the co-author of the books Identification of Parametric Models from Experimental Data (with Eric Walter, Springer, 1997) and Dynamical Search: Applications of Dynamical Systems in Search and Optimization (with Henry P. Wynn and Anatoly A. Zhigljavsky, Chapman & Hall/CRC Press, 2000).
Andrej P\'azman is Professor of Probability and Statistics at the Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Slovakia. He has been Head of the Department of Probability and Statistics (1992-1998) and Head of the Section of Mathematics of his faculty (1999-2001), and he is an elected member of the Learned Society of the Slovak Academy of Sciences. He is the author of the books Foundations of Optimum Experimental Design (Reidel, Kluwer group, 1986) and Nonlinear Statistical Models (Kluwer, 1993).
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Softcover. Etat : Very Good. No Jacket. First edition. 1st 2013. Very good condition. Lecture Notes in Statistics 212. A comprehensive coverage of the various aspects of experimental design for nonlinear models. Cardwraps. Ex-lib with minimal stamps. Covers lightly scuffed and creased. A few biro tick marks to references. Packaged with care and promptly dispatched! N° de réf. du vendeur 1821065
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensive coverage of the various aspects of experimental design for nonlinear models. The book contains original contributions to the theory of optimal experiments that will interest students and researchers in the field. Practitionners motivated by applications will find valuable tools to help them designing their experiments. The first three chapters expose the connections between the asymptotic properties of estimators in parametric models and experimental design, with more emphasis than usual on some particular aspects like the estimation of a nonlinear function of the model parameters, models with heteroscedastic errors, etc. Classical optimality criteria based on those asymptotic properties are then presented thoroughly in a special chapter. Three chapters are dedicated to specific issues raised by nonlinear models. The construction of design criteria derived from non-asymptotic considerations (small-sample situation) is detailed. The connection between design and identifiability/estimability issues is investigated. Several approaches are presented to face the problem caused by the dependence of an optimal design on the value of the parameters to be estimated. A survey of algorithmic methods for the construction of optimal designs is provided. 416 pp. Englisch. N° de réf. du vendeur 9781461463627
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers many important aspects of experimental design, especially as it relates to unpredictable models and data sets Special section on small samples sizes and missing/truncated and imputed data Provides information on small sample size, as. N° de réf. du vendeur 4199048
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