Ebook: Optimal Design and Related Areas in Optimization and Statistics
- Tags: Operations Research Mathematical Programming, Statistics general, Optimization, Algorithm Analysis and Problem Complexity, Algorithms
- Series: Springer Optimization and Its Applications 28
- Year: 2009
- Publisher: Springer-Verlag New York
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages I-XV
W-Iterations and Ripples Therefrom....Pages 1-12
Studying Convergence of Gradient Algorithms Via Optimal Experimental Design Theory....Pages 13-37
A Dynamical-System Analysis of the Optimum s-Gradient Algorithm....Pages 39-80
Bivariate Dependence Orderings for Unordered Categorical Variables....Pages 81-96
Methods in Algebraic Statistics for the Design of Experiments....Pages 97-132
The Geometry of Causal Probability Trees that are Algebraically Constrained....Pages 133-154
Bayes Nets of Time Series: Stochastic Realizations and Projections....Pages 155-166
Asymptotic Normality of Nonlinear Least Squares under Singular Experimental Designs....Pages 167-191
Robust Estimators in Non-linear Regression Models with Long-Range Dependence....Pages 193-221
Back Matter....Pages 223-224
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.
Content:
Front Matter....Pages I-XV
W-Iterations and Ripples Therefrom....Pages 1-12
Studying Convergence of Gradient Algorithms Via Optimal Experimental Design Theory....Pages 13-37
A Dynamical-System Analysis of the Optimum s-Gradient Algorithm....Pages 39-80
Bivariate Dependence Orderings for Unordered Categorical Variables....Pages 81-96
Methods in Algebraic Statistics for the Design of Experiments....Pages 97-132
The Geometry of Causal Probability Trees that are Algebraically Constrained....Pages 133-154
Bayes Nets of Time Series: Stochastic Realizations and Projections....Pages 155-166
Asymptotic Normality of Nonlinear Least Squares under Singular Experimental Designs....Pages 167-191
Robust Estimators in Non-linear Regression Models with Long-Range Dependence....Pages 193-221
Back Matter....Pages 223-224
....