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El. knyga: Handbook of Design and Analysis of Experiments

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Edited by (Iowa State University, Ames, USA), Edited by (Arizona State University, USA), Edited by (Simon Fraser University, Burnaby, Canada), Edited by (Ohio State University, USA)
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A broad reference to the current theory, methodology, and application of designed statistical experiments and their analysis, the self-contained and cross-referenced chapters cover general principles, designs for linear models, designs accommodating multiple factors, optimal design for nonlinear and spatial models, computer experiments, cross-cutting issues, and design for contemporary applications. Among specific topics are response surface experiments and designs, non-regular factorial and supersaturated designs, the algebraic method in experimental design, Latin hypercubes and space-filling designs, and plate designs in high-throughput screening experiments for drug discovery. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)

Handbook of Design and Analysis of Experiments provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook gives a unified treatment of a wide range of topics, covering the latest developments.

This carefully edited collection of 25 chapters in seven sections synthesizes the state of the art in the theory and applications of designed experiments and their analyses. Written by leading researchers in the field, the chapters offer a balanced blend of methodology and applications.

The first section presents a historical look at experimental design and the fundamental theory of parameter estimation in linear models. The second section deals with settings such as response surfaces and block designs in which the response is modeled by a linear model, the third section covers designs with multiple factors (both treatment and blocking factors), and the fourth section presents optimal designs for generalized linear models, other nonlinear models, and spatial models. The fifth section addresses issues involved in designing various computer experiments. The sixth section explores "cross-cutting" issues relevant to all experimental designs, including robustness and algorithms. The final section illustrates the application of experimental design in recently developed areas.

This comprehensive handbook equips new researchers with a broad understanding of the field’s numerous techniques and applications. The book is also a valuable reference for more experienced research statisticians working in engineering and manufacturing, the basic sciences, and any discipline that depends on controlled experimental investigation.

Recenzijos

"The volumes should be of primary interest to researchers and graduate students from (bio)statistics, but also appeal to scientists where the methodology is applied to real problems. Each section contains between two and five chapters. All chapters have been written by leading researchers. Most of the chapters indeed provide a thorough overview of the state of the art in the theory of a specific subfield of design and analysis of experiments." Peter Goos, KU Leuven University of Antwerp, in Journal of the American Statistical Association, May 2017

"This handbook contains 25 thoughtfully assembled articles that have been written by leading researchers in the field of experimental design. The themes addressed by these articles are theories and computational methods in experimental design. They are well organized in seven sections that cover classical and new approaches for designing scientific experiments. Each section can be read independently from the others, and all articles within a section provide excellent references for further reading. The reader can find here a rich theory and methodology in understanding traditional and new problems in experimental design, mostly from the frequentist point of view. At times, the material gets deep and technical but there are many useful references on theoretical and computational issues, which can be found throughout the book. Although it is hard to cover all existing research in experimental design, this handbook manages to give a comprehensive review of many fundamental approaches in experimental design. It is undoubtedly a valuable guide for researchers in statistics, as well as practitioners in the fields of engineering, medicine, biology, or any other discipline that uses experimental investigation. This book could be of value for graduate courses in advanced experimental design with a focus on optimal design theory. It could also be suitable for use as an additional text in any course in adv

Preface xi
Editors xv
Contributors xvii
Section I General Principles
1 History and Overview of Design and Analysis of Experiments
3(60)
Klaus Hinkelmann
2 Introduction to Linear Models
63(36)
Linda M. Haines
Section II Designs for Linear Models
3 Blocking with Independent Responses
99(60)
John P. Morgan
4 Crossover Designs
159(38)
Mausumi Bose
Aloke Dey
5 Response Surface Experiments and Designs
197(40)
Andre I. Khuri
Siuli Mukhopadhyay
6 Design for Linear Regression Models with Correlated Errors
237(42)
Holger Dette
Andrey Pepelyshev
Anatoly Zhigljavsky
Section III Designs Accommodating Multiple Factors
7 Regular Fractional Factorial Designs
279(42)
Robert Mee
Angela Dean
8 Multistratum Fractional Factorial Designs
321(18)
Derek Bingham
9 Nonregular Factorial and Supersaturated Designs
339(32)
Hongquan Xu
10 Structures Defined by Factors
371(44)
R.A. Bailey
11 Algebraic Method in Experimental Design
415(42)
Hugo Maruri-Aguilar
Henry P. Wynn
Section IV Optimal Design for Nonlinear and Spatial Models
12 Optimal Design for Nonlinear and Spatial Models: Introduction and Historical Overview
457(14)
Douglas P. Wiens
13 Designs for Generalized Linear Models
471(44)
Anthony C. Atkinson
David C. Woods
14 Designs for Selected Nonlinear Models
515(34)
Stefanie Biedermann
Min Yang
15 Optimal Design for Spatial Models
549(28)
Zhengyuan Zhu
Evangelos Evangelou
Section V Computer Experiments
16 Design of Computer Experiments: Introduction and Background
577(16)
Max D. Morris
Leslie M. Moore
17 Latin Hypercubes and Space-Filling Designs
593(34)
C. Devon Lin
Boxin Tang
18 Design for Sensitivity Analysis
627(48)
William Becker
Andrea Saltelli
19 Expected Improvement Designs
675(44)
William I. Notz
Section VI Cross-Cutting Issues
20 Robustness of Design
719(36)
Douglas P. Wiens
21 Algorithmic Searches for Optimal Designs
755(32)
Abhyuday Mandal
Weng Kee Wong
Yaming Yu
Section VII Design for Contemporary Applications
22 Design for Discrete Choice Experiments
787(46)
Heiko Grossmann
Rainer Schwabe
23 Plate Designs in High-Throughput Screening Experiments for Drug Discovery
833(24)
Xianggui Qu
Stanley Young
24 Up-and-Down Designs for Dose-Finding
857(38)
Nancy Flournoy
Assaf P. Oron
25 Optimal Design for Event-Related fMRI Studies
895(30)
Jason Ming-Hung Kao
John Stufken
Index 925
Angela Dean is professor emeritus in the Department of Statistics and a member of the Emeritus Academy at The Ohio State University. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. Her primary research focuses on the design of screening experiments.

Max Morris is professor and chair of the Department of Statistics at Iowa State University, where he also holds a courtesy appointment in the Department of Industrial and Manufacturing Systems Engineering. He is a fellow of the American Statistical Association. His research program focuses on the design and analysis of experiments, with special emphasis on those that involve computer models.

John Stufken is the Charles Wexler Professor in Statistics in the School of Mathematical and Statistical Sciences at Arizona State University. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. His primary area of research interest is the design and analysis of experiments.

Derek Bingham is professor in the Department of Statistics and Actuarial Science at Simon Fraser University, Burnaby. His primary research interests lie in the design and analysis of physical and computer experiments.
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