Alexander Donev's Books

Optimum Experimental Designs, With SAS

By: Anthony Atkinson,Alexander Donev,Randall Tobias

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Optimum Experimental Designs, With SAS

By: Anthony Atkinson,Alexander Donev,Randall Tobias

Experiments on patients, processes or plants all have random error, making statistical methods essential for their efficient design and analysis. This book presents the theory and methods of optimum experimental design, making them available through the use of SAS programs. Little previous statistical knowledge is assumed. The first part of the book stresses the importance of models in the analysis of data and introduces least squares fitting and simple optimum experimental designs. The second part presents a more detailed discussion of the general theory and of a wide variety of experiments. The book stresses the use of SAS to provide hands-on solutions for the construction of designs in both standard and non-standard situations. The mathematical theory of the designs is developed in parallel with their construction in SAS, so providing motivation for the development of the subject. Many chapters cover self-contained topics drawn from science, engineering and pharmaceutical investigations, such as response surface designs, blocking of experiments, designs for mixture experiments and for nonlinear and generalized linear models. Understanding is aided by the provision of "SAS tasks" after most chapters as well as by more traditional exercises and a fully supported website. The authors are leading experts in key fields and this book is ideal for statisticians and scientists in academia, research and the process and pharmaceutical industries.

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528

Publisher

OUP Oxford

Published Date

ISBN 10

0191537942

ISBN 13

9780191537943

Cooperative Task-Oriented Computing

Algorithms and Complexity

By: Chryssis Georgiou,Alexander Shvartsman

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Cooperative Task-Oriented Computing

Algorithms and Complexity

By: Chryssis Georgiou,Alexander Shvartsman

Cooperative network supercomputing is becoming increasingly popular for harnessing the power of the global Internet computing platform. A typical Internet supercomputer consists of a master computer or server and a large number of computers called workers, performing computation on behalf of the master. Despite the simplicity and benefits of a single master approach, as the scale of such computing environments grows, it becomes unrealistic to assume the existence of the infallible master that is able to coordinate the activities of multitudes of workers. Large-scale distributed systems are inherently dynamic and are subject to perturbations, such as failures of computers and network links, thus it is also necessary to consider fully distributed peer-to-peer solutions. We present a study of cooperative computing with the focus on modeling distributed computing settings, algorithmic techniques enabling one to combine efficiency and fault-tolerance in distributed systems, and the exposition of trade-offs between efficiency and fault-tolerance for robust cooperative computing. The focus of the exposition is on the abstract problem, called Do-All, and formulated in terms of a system of cooperating processors that together need to perform a collection of tasks in the presence of adversity. Our presentation deals with models, algorithmic techniques, and analysis. Our goal is to present the most interesting approaches to algorithm design and analysis leading to many fundamental results in cooperative distributed computing. The algorithms selected for inclusion are among the most efficient that additionally serve as good pedagogical examples. Each chapter concludes with exercises and bibliographic notes that include a wealth of references to related work and relevant advanced results. Table of Contents: Introduction / Distributed Cooperation and Adversity / Paradigms and Techniques / Shared-Memory Algorithms / Message-Passing Algorithms / The Do-All Problem in Other Settings / Bibliography / Authors' Biographies

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Page Count

155

Publisher

Springer Nature

Published Date

ISBN 10

3031020057

ISBN 13

9783031020056

Model Calibration and Parameter Estimation

For Environmental and Water Resource Systems

By: Ne-Zheng Sun,Alexander Sun

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Model Calibration and Parameter Estimation

For Environmental and Water Resource Systems

By: Ne-Zheng Sun,Alexander Sun

This three-part book provides a comprehensive and systematic introduction to these challenging topics such as model calibration, parameter estimation, reliability assessment, and data collection design. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get familiar with advanced methods for modeling complex systems. Algorithms for mathematical tools used in this book, such as numerical optimization, automatic differentiation, adaptive parameterization, hierarchical Bayesian, metamodeling, Markov chain Monte Carlo, are covered in details. This book can be used as a reference for graduate and upper level undergraduate students majoring in environmental engineering, hydrology, and geosciences. It also serves as an essential reference book for professionals such as petroleum engineers, mining engineers, chemists, mechanical engineers, biologists, biology and medical engineering, applied mathematicians, and others who perform mathematical modeling.

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Page Count

638

Publisher

Springer

Published Date

ISBN 10

1493923234

ISBN 13

9781493923236

Taking Stock of Shock

Social Consequences of the 1989 Revolutions

By: Kristen Ghodsee,Mitchell Alexander Orenstein

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Taking Stock of Shock

Social Consequences of the 1989 Revolutions

By: Kristen Ghodsee,Mitchell Alexander Orenstein

Introduction: Transition from communism - qualified success or utter catastrophe? -- The plan for a J-curve transition -- Plan meets reality -- Modifying the framework -- Counter-narratives of catastrophe -- Where have all the people gone? -- The mortality crisis -- Collapse in fertility -- Outmigration crisis -- Disappointment with transition -- Public opinion of winners and losers -- Evaluations shift over time -- Towards a new social contract? -- Portraits of desperation -- Resistance is futile -- Return to the past -- The patriotism of despair -- Conclusion: Towards an inclusive prosperity.

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305

Publisher

Oxford University Press

Published Date

ISBN 10

0197549233

ISBN 13

9780197549230

Optimum Experimental Designs, With SAS

By: Anthony Atkinson,Alexander Donev,Randall Tobias

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Optimum Experimental Designs, With SAS

By: Anthony Atkinson,Alexander Donev,Randall Tobias

Experiments in the field and in the laboratory cannot avoid random error and statistical methods are essential for their efficient design and analysis. Authored by leading experts in key fields, this text provides many examples of SAS code, results, plots and tables, along with a fully supported website.

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Page Count

528

Publisher

Oxford University Press, USA

Published Date

ISBN 10

0199296596

ISBN 13

9780199296590

Book's by Alexander Donev

Optimum Experimental Designs, With SAS

Optimum Experimental Designs, With SAS

Cooperative Task-Oriented Computing

Cooperative Task-Oriented Computing

Model Calibration and Parameter Estimation

Model Calibration and Parameter Estimation

Taking Stock of Shock

Taking Stock of Shock

Optimum Experimental Designs, With SAS

Optimum Experimental Designs, With SAS

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