Description
Handbook of Design and Analysis of Experiments
Chapman & Hall/CRC Handbooks of Modern Statistical Methods Series
Coordinators: Dean Angela, Morris Max, Stufken John, Bingham Derek
Language: EnglishSubject for Handbook of Design and Analysis of Experiments:
Keywords
Design D1; Orthogonal Latin Hypercube; experimental designs; Defining Contrast Subgroup; design and analysis of computer experiments; Approximate Optimal Designs; designs for nonlinear models; Regular Fractional Factorial Designs; optimal design of experiments; Maximin Distance; response surfaces and block designs; BIBD; spatial models; Latin Hypercube Designs; algorithms for design problems; Fractional Factorial Design; optimal designs for linear models; Row Column Designs; fractional factorial designs; Latin Hypercube; multifactor designs; Orthogonal Array; designed experiments and their analyses; Optimal Design; Maximin Design; Half Normal Plot; Latin Squares; Generalized Word Length Pattern; Fractional Factorial; Approximate Design; Design D0; Orthogonal Latin Hypercube Designs; BIB Design; Defining Relation; Regular Fraction; Low Discrepancy Sequences
Publication date: 06-2020
· 17.8x25.4 cm · Paperback
Publication date: 06-2015
940 p. · 17.8x25.4 cm · Hardback
Description
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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. Designs for Linear Models. Designs Accommodating Multiple Factor. Optimal Design for Nonlinear and Spatial Models. Computer Experiments. Cross-Cutting Issues. Designs for Contemporary Applications. Index.
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.