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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.



General Principles

History and Overview of Design and Analysis of Experiments Klaus Hinkelmann

Introduction to Linear Models Linda M. Haines

Designs for Linear Models

Blocking with Independent Responses John P. Morgan

Crossover Designs Mausumi Bose and Aloke Dey

Response Surface Experiments and Designs André I. Khuri and Siuli Mukhopadhyay

Design for Linear Regression Models with Correlated Errors Holger Dette, Andrey Pepelyshev, and Anatoly Zhigljavsky

Designs Accommodating Multiple Factor

Regular Fractional Factorial Designs Robert Mee and Angela Dean

Multistratum Fractional Factorial Designs Derek Bingham

Nonregular Factorial and Supersaturated Designs Hongquan Xu

Structures Defined by Factors R.A. Bailey

Algebraic Method in Experimental Design Hugo Maruri-Aguilar and Henry P. Wynn

Optimal Design for Nonlinear and Spatial Models

Optimal Design for Nonlinear and Spatial Models: Introduction and Historical Overview Douglas P. Wiens

Designs for Generalized Linear Models Anthony C. Atkinson and David C. Woods

Designs for Selected Nonlinear Models Stefanie Biedermann and Min Yang

Optimal Design for Spatial Models Zhengyuan Zhu and Evangelos Evangelou

Computer Experiments

Design of Computer Experiments: Introduction and Background Max Morris and Leslie Moore

Latin Hypercubes and Space-Filling Designs C. Devon Lin and Boxin Tang

Design for Sensitivity Analysis William Becker and Andrea Saltelli

Expected Improvement Designs William I. Notz

Cross-Cutting Issues

Robustness of Design Douglas P. Wiens

Algorithmic Searches for Optimal Designs Abhyuday Mandal, Weng Kee Wong, and Yaming Yu

Design for Contemporary Applications

Design for Discrete Choice Experiments Heiko Grossmann and Rainer Schwabe

Plate Designs in High-Throughput Screening Experiments for Drug Discovery Xianggui Qu (Harvey) and Stanley Young

Up-and-Down Designs for Dose-Finding Nancy Flournoy and Assaf P. Oron

Optimal Design for Event-Related fMRI Studies Jason Ming-Hung Kao and John Stufken

Index