Provides timely applications, modifications, and extensions of experimental designs for a variety of disciplines
Design and Analysis of Experiments, Volume 3: Special Designs and Applications continues building upon the philosophical foundations of experimental design by providing important, modern applications of experimental design to the many fields that utilize them. The book also presents optimal and efficient designs for practice and covers key topics in current statistical research.
Featuring contributions from leading researchers and academics, the book demonstrates how the presented concepts are used across various fields from genetics and medicinal and pharmaceutical research to manufacturing, engineering, and national security. Each chapter includes an introduction followed by the historical background as well as in-depth procedures that aid in the construction and analysis of the discussed designs. Topical coverage includes:
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Genetic cross experiments, microarray experiments, and variety trials
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Clinical trials, group-sequential designs, and adaptive designs
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Fractional factorial and search, choice, and optimal designs for generalized linear models
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Computer experiments with applications to homeland security
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Robust parameter designs and split-plot type response surface designs
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Analysis of directional data experiments
Throughout the book, illustrative and numerical examples utilize SAS®, JMP®, and R software programs to demonstrate the discussed techniques. Related data sets and software applications are available on the book's related FTP site.
Design and Analysis of Experiments, Volume 3 is an ideal textbook for graduate courses in experimental design and also serves as a practical, hands-on reference for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering, medicine, and business.
Table of Contents:
Preface xvii Contributors xxi
1 Genetic Crosses Experiments 1
Murari Singh, Sudhir Gupta, and Rajender Parsad
1.1 Introduction, 1
1.2 Basic Objectives and Models, 2
1.3 Diallel Mating Design of Type I, 8
1.4 Diallel Crosses: Type II Designs, 14
1.5 Partial Diallel Crosses: No Blocking or Complete Blocks, 25
1.6 Partial Diallel Crosses in Incomplete Blocks, 32
1.7 Optimality, 44
1.8 Robustness, 59
1.9 Three- or Higher-Way Crosses, 61
1.10 Computation, 65
2 Design of Gene Expression Microarray Experiments 73
Dan Nettleton
2.1 Introduction, 73
2.2 Gene Expression Microarray Technology, 74
2.3 Preprocessing of Microarray Fluorescence Intensities, 76
2.4 Introduction to Gene Expression Microarray Experimental Design, 80
2.5 Two-Treatment Experiments Using Two-Color Microarrays, 81
2.6 Two-Color Microarray Experiments Involving More Than Two Treatments, 86
2.7 Multifactor Two-Color Microarray Experiments, 89
2.8 Phase 2 Designs for Complex Phase 1 Designs, 94
3 Spatial Analysis of Agricultural Field Experiments 109
Joanne K. Stringer, Alison B. Smith, and Brian R. Cullis
3.1 Introduction, 109
3.2 Methods to Account for Spatial Variation, 110
3.3 A Spatial Linear Mixed Model, 116
3.4 Analysis of Examples, 122
4 Optimal Designs for Generalized Linear Models 137
John Stufken and Min Yang
4.1 Introduction, 137
4.2 Notation and Basic Concepts, 141
4.3 Tools for Finding Locally Optimal Designs, 145
4.4 GLMs with Two Parameters, 149
4.5 GLMs with Multiple Parameters, 155
4.6 Summary and Concluding Comments, 161
5 Design and Analysis of Randomized Clinical Trials 165
Janet Wittes and Zi-Fan Yu
5.1 Overview, 165
5.2 Components of a Randomized Clinical Trial, 168
5.3 Bias, 175
5.4 Statistical Analysis of Randomized Clinical Trials, 182
5.5 Failure Time Studies, 184
5.6 Other Topics, 206
6 Monitoring Randomized Clinical Trials 213
Eric S. Leifer and Nancy L. Geller
6.1 Introduction, 213
6.2 Normally Distributed Outcomes, 215
6.3 Brownian Motion Properties, 217
6.4 Brief Historical Overview of Group Sequential Methods, 219
6.5 Dichotomous Outcomes, 223
6.6 Time-to-Event Outcomes, 225
6.7 Unconditional Power, 227
6.8 Conditional Power, 229
6.9 Spending Functions, 232
6.10 Flexibility and Properties of Spending Functions, 233
6.11 Modifying the Trial’s Sample Size Based on a Nuisance Parameter, 235
6.12 Sample Size Modification Based on the Interim Treatment Effect, 240
6.13 Concluding Remarks, 246
7 Adaptive Randomization in Clinical Trials 251
Lanju Zhang and William F. Rosenberger
7.1 Introduction, 251
7.2 Adaptive Randomization Procedures, 252
7.3 Likelihood-Based Inference, 264
7.4 Randomization-Based Inference, 269
7.5 Conclusions and Practical Considerations, 276
8 Search Linear Model for Identification and Discrimination 283
Subir Ghosh
8.1 Introduction, 283
8.2 General Linear Model with Fixed Effects, 284
8.3 Search Linear Model, 285
8.4 Applications, 288
8.5 Effects of Noise in Performance Comparison, 293
9 Minimum Aberration and Related Criteria for Fractional Factorial Designs 299
Hegang H. Chen and Ching-Shui Cheng
9.1 Introduction, 299
9.2 Projections of Fractional Factorial Designs, 302
9.3 Estimation Capacity, 304
9.4 Clear Two-Factor Interactions, 307
9.5 Estimation Index, 310
9.6 Estimation Index, Minimum Aberration, and Maximum Estimation Capacity, 314
9.7 Complementary Design Theory for Minimum Aberration Designs, 315
9.8 Nonregular Designs and Orthogonal Arrays, 317
9.9 Generalized Minimum Aberration, 320
9.10 Optimal Fractional Factorial Block Designs, 322
10 Designs for Choice Experiments for the Multinomial Logit Model 331
Deborah J. Street and Leonie Burgess
10.1 Introduction, 331
10.2 Definitions, 332
10.3 The MNL Model, 335
10.4 Design Comparisons, 338
10.5 Optimal Designs for DCEs, 340
10.6 Using Combinatorial Designs to Construct DCEs, 364
10.7 Bayesian Work, 368
10.8 Best–Worst Experiments, 368
10.9 Miscellaneous Topics, 370
11 Computer Experiments 379
Max D. Morris
11.1 Introduction, 379
11.2 Sensitivity/Uncertainty Analysis, 382
11.3 Gaussian Stochastic Process Models, 385
11.4 Inference, 389
11.5 Experimental Designs, 398
11.6 Multivariate Output, 403
11.7 Multiple Data Sources, 406
11.8 Conclusion, 409
12 Designs for Large-Scale Simulation Experiments, with Applications to Defense and Homeland Security 413
Susan M. Sanchez, Thomas W. Lucas, Paul J. Sanchez, Christopher J. Nannini, and Hong Wan
12.1 Introduction, 413
12.2 Philosophy: Evolution of Computational Experiments, 414
12.3 Application: U.S. Army Unmanned Aerial Vehicle (UAV) Mix Study, 422
12.4 Parting Thoughts, 437
13 Robust Parameter Designs 443
Timothy J. Robinson and Christine M. Anderson-Cook
13.1 Introduction, 443
13.2 Taguchi Signal-to-Noise Ratio Approach, 445
13.3 Dual Model Response Surface Methodology, 448
13.4 Single Model Response Surface Methods Using Combined Arrays, 451
13.5 Computer Generated Combined Arrays, 461
13.6 RPD Involving Quantitative and Qualitative Factors, 465
13.7 Conclusions, 466
14 Split-Plot Response Surface Designs 471
G. Geoffrey Vining
14.1 Introduction, 471
14.2 Differences between Agricultural and Industrial Experimentation, 472
14.3 OLS–GLS Equivalent Second-Order Split-Plot Designs and Analysis, 482
14.4 Exact Tests for the Coeffi cients, 488
14.5 Proper Residuals for Checking Assumptions, 493
14.6 "Optimal" Second-Order Split-Plot Designs, 496
15 Design and Analysis of Experiments for Directional Data 501
Sango B. Otieno and Christine M. Anderson-Cook
15.1 Summary, 501
15.2 Introduction and Historical Background, 501
15.3 ANOVA for Circular Data, 509
15.4 ANOVA for Cylindrical Data, 521
15.5 ANOVA for Spherical Data, 524
15.6 Conclusions, 530
References, 531
Author Index 533
Subject Index 545
About the Author :
KLAUS HINKELMANN, PhD, is Professor Emeritus of Statistics at the Virginia Polytechnic Institute and State University Department of Statistics. A Fellow of the American Statistical Association and the American Association for the Advancement of Science, Dr. Hinkelmann has published extensively in the areas of design of experiments, statistical methods, and biometry.
Review :
“The presentation by Wiley is superb, as we have come to expect. All in all, this is a first class offering.”( International Statistical Review, 1 October 2012)
