Handbook of Design and Analysis of Experiments

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advanced experimental design strategies
algorithms for design problems
Approximate Design
Approximate Optimal Designs
array
BIB Design
BIBD
block design analysis
Category=PBT
Defining Contrast Subgroup
Defining Relation
design and analysis of computer experiments
Design D0
Design D1
designed experiments and their analyses
designs for nonlinear models
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
experimental designs
factorial
fractional
Fractional Factorial
Fractional Factorial Design
fractional factorial designs
Generalized Word Length Pattern
half
Half Normal Plot
information
Latin Hypercube
Latin Hypercube Designs
Latin Squares
Low Discrepancy Sequences
matrix
Maximin Design
Maximin Distance
multifactor designs
nonlinear model optimization
normal
optimal
Optimal Design
optimal design of experiments
optimal designs for linear models
orthogonal
Orthogonal Array
Orthogonal Latin Hypercube
Orthogonal Latin Hypercube Designs
parameter estimation methods
plots
Regular Fraction
Regular Fractional Factorial Designs
response surface methodology
response surfaces and block designs
Row Column Designs
spatial models
spatial statistics techniques
statistical experiment planning

Product details

  • ISBN 9780367570415
  • Weight: 1780g
  • Dimensions: 178 x 254mm
  • Publication Date: 30 Jun 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

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.