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Optimal Design and Related Areas in Optimization and Statistics [electronic resource] / edited by Luc Pronzato, Anatoly Zhigljavsky.

Contributor(s): Pronzato, Luc [editor.] | Zhigljavsky, Anatoly [editor.] | SpringerLink (Online service).
Material type: TextTextSeries: Springer Optimization and Its Applications: 28Publisher: New York, NY : Springer New York, 2009Description: XV, 224 p. 23 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387799360.Subject(s): Mathematical optimization | Distribution (Probability theory | Statistics | Computer software | Algorithms | Optimization | Probability Theory and Stochastic Processes | Operations Research, Management Science | Statistics, general | Algorithm Analysis and Problem Complexity | AlgorithmsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519.6 Online resources: Click here to access online
Contents:
W-Iterations and Ripples Therefrom -- Studying Convergence of Gradient Algorithms Via Optimal Experimental Design Theory -- A Dynamical-System Analysis of the Optimum s-Gradient Algorithm -- Bivariate Dependence Orderings for Unordered Categorical Variables -- Methods in Algebraic Statistics for the Design of Experiments -- The Geometry of Causal Probability Trees that are Algebraically Constrained -- Bayes Nets of Time Series: Stochastic Realizations and Projections -- Asymptotic Normality of Nonlinear Least Squares under Singular Experimental Designs -- Robust Estimators in Non-linear Regression Models with Long-Range Dependence.
In: Springer eBooksSummary: This edited volume, dedicated to Henry P. Wynn, reflects his broad range of research interests, focusing in particular on the applications of optimal design theory in optimization and statistics. It covers algorithms for constructing optimal experimental designs, general gradient-type algorithms for convex optimization, majorization and stochastic ordering, algebraic statistics, Bayesian networks and nonlinear regression. Written by leading specialists in the field, each chapter contains a survey of the existing literature along with substantial new material. This work will appeal to both the specialist and the non-expert in the areas covered. By attracting the attention of experts in optimization to important interconnected areas, it should help stimulate further research with a potential impact on applications.
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W-Iterations and Ripples Therefrom -- Studying Convergence of Gradient Algorithms Via Optimal Experimental Design Theory -- A Dynamical-System Analysis of the Optimum s-Gradient Algorithm -- Bivariate Dependence Orderings for Unordered Categorical Variables -- Methods in Algebraic Statistics for the Design of Experiments -- The Geometry of Causal Probability Trees that are Algebraically Constrained -- Bayes Nets of Time Series: Stochastic Realizations and Projections -- Asymptotic Normality of Nonlinear Least Squares under Singular Experimental Designs -- Robust Estimators in Non-linear Regression Models with Long-Range Dependence.

This edited volume, dedicated to Henry P. Wynn, reflects his broad range of research interests, focusing in particular on the applications of optimal design theory in optimization and statistics. It covers algorithms for constructing optimal experimental designs, general gradient-type algorithms for convex optimization, majorization and stochastic ordering, algebraic statistics, Bayesian networks and nonlinear regression. Written by leading specialists in the field, each chapter contains a survey of the existing literature along with substantial new material. This work will appeal to both the specialist and the non-expert in the areas covered. By attracting the attention of experts in optimization to important interconnected areas, it should help stimulate further research with a potential impact on applications.

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