5 edition of Optimal design and related areas in optimization and statistics found in the catalog.
Includes bibliographical references and index.
|Statement||edited by Luc Pronzato, Anatoly Zhigljavsky.|
|Series||Springer optimization and its applications -- 28|
|Contributions||Pronzato, Luc, 1959-, Zhigli︠a︡vskiĭ, A. A.|
|LC Classifications||QA279 .O667 2009|
|The Physical Object|
|Pagination||xv, 224 p. :|
|Number of Pages||224|
|LC Control Number||2008940068|
Nemirovski - Princeton University PressWritten by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of this relatively new approach to optimization. This covers both linear and non-linear models. Cui et al. The experimental results show that compared with MIP model solved by genetic algorithm GA and benders decomposition algorithm BDthe novel NFP model and the improved network simplex algorithm are effective and more efficient. The results based on samples from the US textile industry show that by using intelligent agents under the CSET model it is possible to automatically find an ideal group of trading partners from a supply mesh out of many possibilities. Zhi-hui et al.
Li et al. Some of these methods are discussed by Atkinson, Donev and Tobias and in the paper by Hardin and Sloane. See also. In English, two early contributions were made by Charles S.
Li et al. Ke et al. The experimenter must specify a model for the design and an optimality-criterion before the method can compute an optimal design. Lin et al. In the estimation theory for statistical model s with one real parameterthe reciprocal of the variance of an "efficient" estimator is called the " Fisher information " for that estimator.
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This helps ensure that, for the known uncertainties in the design inputs, the engine efficiency is always acceptable and the pollution requirements are never violated. The applicability of the proposed approach to the recommendation of the initiators on a group-buying website is examined. Other examples arise in sampling problems.
The main thrust of this, the third conference of the series is related to engineering optimization including both manufacture and parametric design. Optimality of Regression Experiments.
See also: Nuisance parameter In many applications, the statistician is most concerned with a "parameter of interest" rather than with "nuisance parameters".
Mathematical Programming, A, — For seven of the nine pairs in which optimal has become the preferred adjective form, the point at which the optimal form passed the optimum form in frequency was between and ; in the other two, the crossover occurred in the early s. Charles S. Nonetheless, such optimal probability-measure designs can be discretized to furnish approximately optimal designs.
Nonlinear Programming Techniques. The experimental results show that compared with MIP model solved by genetic algorithm GA and benders decomposition algorithm BDthe novel NFP model and the improved network simplex Optimal design and related areas in optimization and statistics book are effective and more efficient.
Sequential analysis was pioneered by Abraham Wald. The mathematical techniques of optimization are fundamentalto statistical theory and practice. Computational Optimization and Applications, 22, 5— Linear Programming Techniques.
I-optimality integrated A second criterion on prediction variance is I-optimality, which seeks to minimize the average prediction variance over the design space. Huang presents a coordination scheme for a single period newsvendor problem when both supplier and retailer of the supply chain agree to change the business operation from a market decision power sharing system to a unique decision-maker system.
Without investigating the behavior around the optimal design point, it is difficult to avoid unstable optima that can result in undesirable behavior given normal variation in the final product.
Acta Applicandae Mathematicae, 67, 1— ResponseSurface Methods. With SmartUQ, the use of emulators as light weight stand-ins for complex systems can result in significant decreases in both the number of system evaluations and the clock time required to get solutions.
Dynamic Programming and Approximation.This book, the first on these topics, addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space, which has applications in computational geometry, data representations, and optimal design in statistics.
The book covers the formulation of this and related problems, theoretical properties of their. Home» MAA Publications» MAA Reviews» Optimal Design and Related Areas in Optimization and Statistics.
Optimal Design and Related Areas in Optimization and Statistics. Luc Prozato and Anatoly Zhigljavsky, editors H.P.
Wynn, A. Zhigljavsky: Studying Convergence of Gradient Algorithms via Optimal Experimental Design Theory.- L.
Pronzato. Haycroft R., Pronzato L., Wynn H.P., Zhigljavsky A. () Studying Convergence of Gradient Algorithms Via Optimal Experimental Design Theory.
In: Pronzato L., Zhigljavsky A. (eds) Optimal Design and Related Areas in Optimization and Statistics. Springer Optimization and Its Cited by: 3.Chapter D-Optimal Designs Introduction Pdf procedure generates D-optimal designs for multi-factor experiments with both quantitative and qualitative factors.
The factors can have a mixed number of levels. Hence, you could use this procedure to design an.Haycroft R., Pronzato L., Wynn H.P., Zhigljavsky A. () Studying Convergence of Gradient Algorithms Via Optimal Experimental Design Theory.
In: Pronzato L., Zhigljavsky A. (eds) Optimal Design and Related Areas in Optimization and Statistics. Springer Optimization and Its Cited by: 3.Ebook criteria such as those of least squares, maximum likelihood, and minimum chi-square utilize classical techniques of maxima and minima.
In other areas of statistics, such as in design of experiments, survey sampling, testing hypotheses, and regression analysis, extensive use of .