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008			181113s2011 xx o ||||0 eng d
020			_a9781118147665 _q(electronic bk.)
020			_z9780470530689
035			_a(MiAaPQ)EBC818475
035			_a(Au-PeEL)EBL818475
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035			_a(CaONFJC)MIL340958
035			_a(OCoLC)771875964
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050		4	_aQA279 -- .D47 2012eb
082	0		_a001.434
100	1		_aHinkelmann, Klaus.
245	1	0	_aDesign and Analysis of Experiments : _bSpecial Designs and Applications.
250			_a1st ed.
264		1	_aNew York : _bJohn Wiley & Sons, Incorporated, _c2011.
264		4	_c©2012.
300			_a1 online resource (596 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aWiley Series in Probability and Statistics Ser. ; _vv.810
505	0		_aIntro -- Design and Analysis of Experiments -- Contents -- Preface -- Contributors -- CHAPTER 1: Genetic Crosses Experiments -- 1.1 INTRODUCTION -- 1.2 BASIC OBJECTIVES AND MODELS -- 1.2.1 Generation Mean Analysis -- 1.2.2 Generation Variance Analysis -- 1.2.3 Covariance between Relatives -- 1.2.4 Mating (M) and Environmental (E) Designs -- 1.2.5 Fixed Effects and Random Effects Models -- 1.3 DIALLEL MATING DESIGN OF TYPE I -- 1.3.1 North Carolina Design I (NCI) -- 1.3.2 North Carolina Design II (NCII) -- 1.3.3 Sets of North Carolina Design II -- 1.3.4 North Carolina Design III (NCIII) -- 1.3.5 Line × Tester Approach -- 1.3.6 A Modified Line × Tester Approach -- 1.4 DIALLEL CROSSES: TYPE II DESIGNS -- 1.4.1 Hayman Approach for Diallel Analysis -- 1.4.2 Griffing's Method -- 1.5 PARTIAL DIALLEL CROSSES: NO BLOCKING OR COMPLETE BLOCKS -- 1.6 PARTIAL DIALLEL CROSSES IN INCOMPLETE BLOCKS -- 1.6.1 Construction of Mating-Environment Designs -- 1.6.2 Analysis of M-E Design -- 1.6.3 An Example of PDC in Incomplete Blocks -- 1.6.4 Other M-E Designs -- 1.7 OPTIMALITY -- 1.7.1 Optimal CDC Designs for Estimation of gca -- 1.7.2 Optimal Partial Diallel Crosses -- 1.7.3 Estimation of Heritability -- 1.8 ROBUSTNESS -- 1.9 THREE- OR HIGHER-WAY CROSSES -- 1.9.1 Triallel or Three-Way Crosses -- 1.9.2 Double- or Four-Way Crosses -- 1.10 COMPUTATION -- ACKNOWLEDGMENTS -- REFERENCES -- CHAPTER 2: Design of Gene Expression Microarray Experiments -- 2.1 INTRODUCTION -- 2.2 GENE EXPRESSION MICROARRAY TECHNOLOGY -- 2.2.1 Introduction -- 2.2.2 Definition of a Microarray -- 2.2.3 Using Microarrays to Measure Gene Expression -- 2.2.4 Types of Gene Expression in Microarrays -- 2.3 PREPROCESSING OF MICROARRAY FLUORESCENCE INTENSITIES -- 2.3.1 Introduction -- 2.3.2 Background Correction -- 2.3.3 Normalization -- 2.3.4 Summarization.
505	8		_a2.4 INTRODUCTION TO GENE EXPRESSION MICROARRAY EXPERIMENTAL DESIGN -- 2.5 TWO-TREATMENT EXPERIMENTS USING TWO-COLOR MICROARRAYS -- 2.6 TWO-COLOR MICROARRAY EXPERIMENTS INVOLVING MORE THAN TWO TREATMENTS -- 2.7 MULTIFACTOR TWO-COLOR MICROARRAY EXPERIMENTS -- 2.7.1 Introduction -- 2.7.2 Admissible Designs -- 2.7.3 w-Optimal Designs -- 2.7.4 e-Efficiency -- 2.8 PHASE 2 DESIGNS FOR COMPLEX PHASE 1 DESIGNS -- REFERENCES -- CHAPTER 3: Spatial Analysis of Agricultural Field Experiments -- 3.1 INTRODUCTION -- 3.2 METHODS TO ACCOUNT FOR SPATIAL VARIATION -- 3.2.1 Design of Experiments -- 3.2.2 Spatial Analysis Methods -- 3.3 A SPATIAL LINEAR MIXED MODEL -- 3.3.1 Estimation, Prediction and Testing -- 3.3.2 The Spatial Modeling Process -- 3.4 ANALYSIS OF EXAMPLES -- 3.4.1 Herbicide Tolerance Trial -- 3.4.2 Variety Trial -- REFERENCES -- CHAPTER 4: Optimal Designs for Generalized Linear Models -- 4.1 INTRODUCTION -- 4.2 NOTATION AND BASIC CONCEPTS -- 4.2.1 Binary Data -- 4.2.2 Count Data -- 4.2.3 Optimality Criteria -- 4.3 TOOLS FOR FINDING LOCALLY OPTIMAL DESIGNS -- 4.3.1 Traditional Approaches -- 4.3.2 An Analytical Approach -- 4.4 GLMs WITH TWO PARAMETERS -- 4.5 GLMs WITH MULTIPLE PARAMETERS -- 4.5.1 GLMs with Multiple Covariates -- 4.5.2 GLMs with Group Effects -- 4.6 SUMMARY AND CONCLUDING COMMENTS -- ACKNOWLEDGMENTS -- REFERENCES -- CHAPTER 5: Design and Analysis of Randomized Clinical Trials -- 5.1 OVERVIEW -- 5.2 COMPONENTS OF A RANDOMIZED CLINICAL TRIAL -- 5.2.1 Target, or Reference, Population -- 5.2.2 Study Population -- 5.2.3 Outcomes -- 5.2.4 Projected Timeline -- 5.2.5 Choice of Control Group -- 5.3 BIAS -- 5.3.1 Unbiased Entry Criteria and Recruitment -- 5.3.2 Outcome Measures-Unbiased Assessment -- 5.3.3 Once Randomized, Always Analyzed (Intent-to-Treat) -- 5.3.4 Masking Participants, Investigators, and Others.
505	8		_a5.3.5 Noncompliance and Study Dropout -- 5.4 STATISTICAL ANALYSIS OF RANDOMIZED CLINICAL TRIALS -- 5.5 FAILURE TIME STUDIES -- 5.5.1 Basic Theory -- 5.5.2 Actuarial and Product-Limit Survival Curves -- 5.5.3 Exponential Survival, Hazard Rates, and Ratios and Proportional Hazard Ratios -- 5.5.4 The Logrank Family of Tests -- 5.5.5 The Cox Proportional Hazards Model -- 5.5.6 Some Sample SAS Code -- 5.5.7 Some Sample Splus Code -- 5.5.8 Calculations of Number of Replications, or Sample Size -- 5.5.9 Group Sequential Analysis -- 5.6 OTHER TOPICS -- 5.6.1 Multiplicity -- 5.6.2 Subgroups -- 5.6.3 Large, Simple Trials -- 5.6.4 Equivalence and Noninferiority Trials -- REFERENCES -- CHAPTER 6: Monitoring Randomized Clinical Trials -- 6.1 INTRODUCTION -- 6.2 NORMALLY DISTRIBUTED OUTCOMES -- 6.3 BROWNIAN MOTION PROPERTIES -- 6.4 BRIEF HISTORICAL OVERVIEW OF GROUP SEQUENTIAL METHODS -- 6.5 DICHOTOMOUS OUTCOMES -- 6.6 TIME-TO-EVENT OUTCOMES -- 6.7 UNCONDITIONAL POWER -- 6.8 CONDITIONAL POWER -- 6.9 SPENDING FUNCTIONS -- 6.10 FLEXIBILITY AND PROPERTIES OF SPENDING FUNCTIONS -- 6.11 MODIFYING THE TRIAL'S SAMPLE SIZE BASED ON A NUISANCE PARAMETER -- 6.11.1 Sample Size Modification for a Continuous Outcome Based on an Interim Variance Estimate -- 6.11.2 Sample Size Modification for a Dichotomous Outcome Based on an Interim Estimate of the Pooled Event Rate -- 6.12 SAMPLE SIZE MODIFICATION BASED ON THE INTERIM TREATMENT EFFECT -- 6.13 CONCLUDING REMARKS -- REFERENCES -- CHAPTER 7: Adaptive Randomization in Clinical Trials -- 7.1 INTRODUCTION -- 7.2 ADAPTIVE RANDOMIZATION PROCEDURES -- 7.2.1 Restricted Randomization Procedures -- 7.2.2 Covariate-Adaptive Randomization -- 7.2.3 Response-Adaptive Randomization -- 7.3 LIKELIHOOD-BASED INFERENCE -- 7.3.1 Restricted Randomization -- 7.3.2 Covariate-Adaptive Randomization -- 7.3.3 Response-Adaptive Randomization.
505	8		_a7.3.4 Asymptotically "Best" Procedures -- 7.4 RANDOMIZATION-BASED INFERENCE -- 7.4.1 Randomization Tests -- 7.4.2 Monte Carlo Unconditional Tests -- 7.4.3 Monte Carlo Conditional Tests -- 7.4.4 Expanding the Reference Set -- 7.4.5 Stratified Tests -- 7.4.6 Regression Modeling -- 7.4.7 Covariate-Adaptive Randomization -- 7.4.8 Power -- 7.5 CONCLUSIONS AND PRACTICAL CONSIDERATIONS -- ACKNOWLEDGMENT -- REFERENCES -- CHAPTER 8: Search Linear Model for Identification and Discrimination -- 8.1 INTRODUCTION -- 8.2 GENERAL LINEAR MODEL WITH FIXED EFFECTS -- 8.3 SEARCH LINEAR MODEL -- 8.4 APPLICATIONS -- 8.4.1 2m Factorial Designs -- 8.4.2 3m Factorial Experiments -- 8.5 EFFECTS OF NOISE IN PERFORMANCE COMPARISON -- REFERENCES -- CHAPTER 9: Minimum Aberration and Related Criteria for Fractional Factorial Designs -- 9.1 INTRODUCTION -- 9.2 PROJECTIONS OF FRACTIONAL FACTORIAL DESIGNS -- 9.3 ESTIMATION CAPACITY -- 9.4 CLEAR TWO-FACTOR INTERACTIONS -- 9.5 ESTIMATION INDEX -- 9.6 ESTIMATION INDEX, MINIMUM ABERRATION, AND MAXIMUM ESTIMATION CAPACITY -- 9.7 COMPLEMENTARY DESIGN THEORY FOR MINIMUM ABERRATION DESIGNS -- 9.8 NONREGULAR DESIGNS AND ORTHOGONAL ARRAYS -- 9.9 GENERALIZED MINIMUM ABERRATION -- 9.10 OPTIMAL FRACTIONAL FACTORIAL BLOCK DESIGNS -- REFERENCES -- CHAPTER 10: Designs for Choice Experiments for the Multinomial Logit Model -- 10.1 INTRODUCTION -- 10.2 DEFINITIONS -- 10.2.1 Standard Designs -- 10.3 THE MNL MODEL -- 10.4 DESIGN COMPARISONS -- 10.4.1 Optimality -- 10.4.2 Structural Properties -- 10.5 OPTIMAL DESIGNS FOR DCEs -- 10.5.1 Generic Forced Choice DCEs -- 10.5.2 Extensions -- 10.5.3 Alternative-Specific Attributes -- 10.6 USING COMBINATORIAL DESIGNS TO CONSTRUCT DCEs -- 10.6.1 OAs and BIBDs -- 10.6.2 A Recursive Construction using DCEs and BIBDs -- 10.6.3 Using the OA Symbols as Ordered Pairs -- 10.6.4 Using Hadamard Matrices to Construct DCEs.
505	8		_a10.6.5 Partial Profiles -- 10.7 BAYESIAN WORK -- 10.8 BEST-WORST EXPERIMENTS -- 10.8.1 Multiattribute Best-Worst Experiments -- 10.8.2 Attribute-Level Best-Worst Experiments -- 10.9 MISCELLANEOUS TOPICS -- 10.9.1 Other Models -- 10.9.2 Complete Determination of Optimal Designs -- 10.9.3 Analyzing Results from a DCE -- REFERENCES -- CHAPTER 11: Computer Experiments -- 11.1 INTRODUCTION -- 11.1.1 Models -- 11.1.2 Some Notation -- 11.1.3 Computer Experiments -- 11.2 SENSITIVITY/UNCERTAINTY ANALYSIS -- 11.2.1 Descriptive Methods for Local Analysis -- 11.2.2 Methods Based on Input Sampling and Conditional Variance -- 11.2.3 Fourier Amplitude Sensitivity Test -- 11.3 GAUSSIAN STOCHASTIC PROCESS MODELS -- 11.3.1 Model Structure -- 11.3.2 Accommodating Random Noise -- 11.4 INFERENCE -- 11.4.1 Maximum Likelihood Parameter Estimation -- 11.4.2 Numerical Issues -- 11.4.3 Bayesian Approach -- 11.5 EXPERIMENTAL DESIGNS -- 11.5.1 Model-Based Designs -- 11.5.2 Distance-Based Designs -- 11.5.3 Latin Hypercube Designs -- 11.5.4 Uniform Designs -- 11.6 MULTIVARIATE OUTPUT -- 11.6.1 Extending the Univariate GaSP Model -- 11.6.2 Principal Components -- 11.6.3 Derivatives -- 11.7 MULTIPLE DATA SOURCES -- 11.7.1 Multiple Models -- 11.7.2 Model and Reality -- 11.8 CONCLUSION -- REFERENCES -- CHAPTER 12: Designs for Large-Scale Simulation Experiments, with Applications to Defense and Homeland Security -- 12.1 INTRODUCTION -- 12.2 PHILOSOPHY: EVOLUTION OF COMPUTATIONAL EXPERIMENTS -- 12.2.1 Context -- 12.2.2 Why Simulation? -- 12.2.3 Why DOE? -- 12.2.4 Which DOE? -- 12.2.5 Implementing Large-Scale DOE -- 12.3 APPLICATION: U.S. ARMY UNMANNED AERIAL VEHICLE (UAV) MIX STUDY -- 12.3.1 Study Overview -- 12.3.2 Study Goals -- 12.3.3 Experimental Setup -- 12.3.4 Results -- 12.3.5 Descriptive Statistics -- 12.3.6 Interactive Regression Modeling -- 12.3.7 Regression Trees.
505	8		_a12.3.8 Other Useful Plots.
520			_aProvides 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, search, and choice designs, 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®, 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,
520	8		_amedicine, and business.
588			_aDescription based on publisher supplied metadata and other sources.
590			_aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650		0	_aExperimental design.
650		0	_aMathematical statistics.
655		4	_aElectronic books.
776	0	8	_iPrint version: _aHinkelmann, Klaus _tDesign and Analysis of Experiments : Special Designs and Applications _dNew York : John Wiley & Sons, Incorporated,c2011 _z9780470530689
797	2		_aProQuest (Firm)
830		0	_aWiley Series in Probability and Statistics Ser.
856	4	0	_uhttps://ebookcentral.proquest.com/lib/buse-ebooks/detail.action?docID=818475 _zClick to View
999			_c90387 _d90387