Design and Analysis of Experiments, Volume 3

Special Designs and Applications
Herausgegeben von Hinkelmann, Klaus

• Gebundenes Buch

Produktbeschreibung

This third volume in the series discusses special modifications and extensions of designs that arise in certain fields of application such as genetics, bioinformatics, agriculture, medicine, manufacturing, marketing, and more. The book is written by an expert panel of contributors. The text contains an introductory and historical information discusses the latest results and future developments in the field. Volume 3 serves as supplement to both Volume 1 and Volume 2 and offers appropriate, numerical examples using the statistical software SAS. 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; and more.

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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:
Genetic cross experiments, microarray experiments, and variety trials
Clinical trials, group-sequential designs, and adaptive designs
Fractional factorial and search, choice, and optimal designs for generalized linear models
Computer experiments with applications to homeland security
Robust parameter designs and split-plot type response surface designs
Analysis of directional data experiments

Throughout the book, illustrative and numerical examples utilize SAS(r), JMP(r), 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.

Produktdetails

• Wiley Series in Probability and Statistics .3
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 608
• Erscheinungstermin: 14. Februar 2012
• Englisch
• Abmessung: 239mm x 165mm x 33mm
• Gewicht: 916g
• ISBN-13: 9780470530689
• ISBN-10: 0470530685
• Artikelnr.: 34439832

Autorenporträt

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.

Inhaltsangabe

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