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This book discusses special modifications and extensions of designs that arise in certain fields of application such as genetics, bioinformatics, agriculture, medicine, manufacturing, marketing, etc. Well-known and highly-regarded contributors have written individual chapters that have been extensively reviewed by the Editor to ensure that each individual contribution relates to material found in Volumes 1 and 2 of this book series. The chapters in Volume 3 have an introductory/historical component and proceed to a more advanced technical level to discuss the latest results and future developments in the field. Thus, Volume 3 serves as supplement to both Volume 1 and Volume 2 and is also accessible not only to statistics graduate students, but also to non-statistical readers. Where appropriate, numerical examples using the statistical software SAS(r) are included in the book. Topical coverage includes: genetic cross experiments; designs for microarrays; incomplete cross-over designs; clinical trials; group sequential designs; adaptive designs; choice experiments; taste-testing experiments; grazing experiments; inter-cropping and competition designs; greenhouse experiments; long-term agricultural experiments; designs for circular and directional data; trend-free designs; one-at-a-time factorial designs; sequential fractional factorial designs; irregular fractional factorial designs; split-plot type response surface designs; multi-response designs; and computer experiments.
Full Product DetailsAuthor: Klaus Hinkelmann
Publisher: John Wiley and Sons Ltd
Imprint: Wiley-Blackwell (an imprint of John Wiley & Sons Ltd)
Country of Publication: United Kingdom
Dimensions: Width: 16.40cm , Height: 3.50cm , Length: 24.10cm
ISBN 10: 0470530685
Publication Date: 02 March 2012
Audience: Professional and scholarly , Professional & Vocational
Publisher's Status: Active
Availability: To order
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Table of ContentsPreface 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
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)
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.
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