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书名：	Optimum Experimental Designs, with SAS
作者：	Atkinson, Anthony; Donev, Alexander; Tobias, Randall
出版时间：	2007-05-24
ISBN：	9780199296590(P-ISBN) ，9780191537943(O-ISBN)
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Contents PART I: BACKGROUND 1 Introduction 1.1 Some Examples 1.2 Scope and Limitations 1.3 Background Reading 2 Some Key Ideas 2.1 Scaled Variables 2.2 Design Regions 2.3 Random Error 2.4 Unbiasedness, Validity, and Efficiency 3 Experimental Strategies 3.1 Objectives of the Experiment 3.2 Stages in Experimental Research 3.3 The Optimization of Yield 3.4 Further Reading 4 The Choice of a Model 4.1 Linear Models for One Factor 4.2 Non-linear Models 4.3 Interaction 4.4 Response Surface Models 5 Models and Least Squares 5.1 Simple Regression 5.2 Matrices and Experimental Design 5.3 Least Squares 5.4 Further Reading 6 Criteria for a Good Experiment 6.1 Aims of a Good Experiment 6.2 Confidence Regions and the Variance of Prediction 6.3 Contour Plots of Variances for Two-Factor Designs 6.4 Variance–Dispersion Graphs 6.5 Some Criteria for Optimum Experimental Designs 7 Standard Designs 7.1 Introduction 7.2 2[supm)] Factorial Designs 7.3 Blocking 2[supm)] Factorial Designs 7.4 2[supm–f)] Fractional Factorial Designs 7.5 Plackett—Burman Designs 7.6 Composite Designs 7.7 Standard Designs in SAS 7.8 Further Reading 8 The Analysis of Experiments 8.1 Introduction 8.2 Example 1.1 Revisited: The Desorption of Carbon Monoxide 8.3 Example 1.2 Revisited: The Viscosity of Elastomer Blends 8.4 Selecting Effects in Saturated Fractional Factorial Designs 8.5 Robust Design 8.6 Analysing Data with SAS PART II: THEORY AND APPLICATIONS 9 Optimum Design Theory 9.1 Continuous and Exact Designs 9.2 The General Equivalence Theorem 9.3 Exact Designs and the General Equivalence Theorem 9.4 Algorithms for Continuous Designs and the General Equivalence Theorem 9.5 Function Optimization and Continuous Design 9.6 Finding Continuous Optimum Designs Using SAS/IML Software 10 Criteria of Optimality 10.1 A-, D-, and E-optimality 10.2 D[subA)]-optimality Generalized D-optimality)10.3 D[subS)]-optimality 10.4 c-optimality 10.5 Linear Optimality: C- and L-optimality 10.6 V-optimality: Average Variance 10.7 G-optimality 10.8 Compound Design Criteria 10.9 Compound D[subA)]-optimality 10.10 D-optimum Designs for Multivariate Responses 10.11 Further Reading and Other Criteria 11 D-optimum Designs 11.1 Properties of D-optimum Designs 11.2 The Sequential Construction of Optimum Designs 11.3 An Example of Design Efficiency: The Desorption of Carbon Monoxide. Example 1.1 Continued 11.4 Polynomial Regression in One Variable 11.5 Second-order Models in Several Variables 11.6 Further Reading 12 Algorithms for the Construction of Exact D-optimum Designs 12.1 Introduction 12.2 The Exact Design Problem 12.3 Basic Formulae for Exchange Algorithms 12.4 Sequential Algorithms 12.5 Non-sequential Algorithms 12.6 The KL and BLKL Exchange Algorithms 12.7 Example 12.2: Performance of an Internal Combustion Engine 12.8 Other Algorithms and Further Reading 13 Optimum Experimental Design with SAS 13.1 Introduction 13.2 Finding Exact Optimum Designs Using the OPTEX Procedure 13.3 Efficiencies and Coding in OPTEX 13.4 Finding Optimum Designs Over Continuous Regions Using SAS/IML Software 13.5 Finding Exact Optimum Designs Using the ADX Interface 14 Experiments with Both Qualitative and Quantitative Factors 14.1 Introduction 14.2 Continuous Designs 14.3 Exact Designs 14.4 Designs with Qualitative Factors in SAS 14.5 Further Reading 15 Blocking Response Surface Designs 15.1 Introduction 15.2 Models and Design Optimality 15.3 Orthogonal Blocking 15.4 Related Problems and Literature 15.5 Optimum Block Designs in SAS 16 Mixture Experiments 16.1 Introduction 16.2 Models and Designs for Mixture Experiments 16.3 Constrained Mixture Experiments 16.4 Mixture Experiments with Other Factors 16.5 Blocking Mixture Experiments 16.6 The Amount of a Mixture 16.7 Optimum Mixture Designs in SAS 16.8 Further Reading 17 Non-linear Models 17.1 Some Examples 17.2 Parameter Sensitivities and D-optimum Designs 17.3 Strategies for Local Optimality 17.4 Sampling Windows 17.5 Locally c-optimum Designs 17.6 The Analysis of Non-linear Experiments 17.7 A Sequential Experimental Design 17.8 Design for Diffierential Equation Models 17.9 Multivariate Designs 17.10 Optimum Designs for Non-linear Models in SAS 17.11 Further Reading 18 Bayesian Optimum Designs 18.1 Introduction 18.2 A General Equivalence Theorem Incorporating Prior Information 18.3 Bayesian D-optimum Designs 18.4 Bayesian c-optimum Designs 18.5 Sampled Parameter Values 18.6 Discussion 19 Design Augmentation 19.1 Failure of an Experiment 19.2 Design Augmentation and Equivalence Theory 19.3 Examples of Design Augmentation 19.4 Exact Optimum Design Augmentation 19.5 Design Augmentation in SAS 19.6 Further Reading 20 Model Checking and Designs for Discriminating Between Models 20.1 Introduction 20.2 Parsimonious Model Checking 20.3 Examples of Designs for Model Checking 20.4 Example 20.3. A Non-linear Model for Crop Yield and Plant Density 20.5 Exact Model Checking Designs in SAS 20.6 Discriminating Between Two Models 20.7 Sequential Designs for Discriminating Between Two Models 20.8 Developments of T-optimality 20.9 Nested Linear Models and D[subs)]-optimum Designs 20.10 Exact T-optimum Designs in SAS 20.11 The Analysis of T-optimum Designs 20.12 Further Reading 21 Compound Design Criteria 21.1 Introduction 21.2 Design Efficiencies 21.3 Compound Design Criteria 21.4 Polynomials in One Factor 21.5 Model Building and Parameter Estimation 21.6 Non-linear Models 21.7 Discrimination Between Models 21.8 DT-Optimum Designs 21.9 CD-Optimum Designs 21.10 Optimizing Compound Design Criteria in SAS 21.11 Further Reading 22 Generalized Linear Models 22.1 Introduction 22.2 Weighted Least Squares 22.3 Generalized Linear Models 22.4 Models and Designs for Binomial Data 22.5 Optimum Design for Gamma Models 22.6 Designs for Generalized Linear Models in SAS 22.7 Further Reading 23 Response Transformation and Structured Variances 23.1 Introduction 23.2 Transformation of the Response 23.3 Design for a Response Transformation 23.4 Response Transformations in Non-linear models 23.5 Robust and Compound Designs 23.6 Structured Mean–Variance Relationships 24 Time-dependent Models with Correlated Observations 24.1 Introduction 24.2 The Statistical Model 24.3 Numerical Example 24.4 Multiple Independent Series 24.5 Discussion and Further Reading 25 Further Topics 25.1 Introduction 25.2 Crossover Designs 25.3 Biased-coin Designs for Clinical Trials 25.4 Adaptive Designs for Clinical Trials 25.5 Population Designs 25.6 Designs Over Time 25.7 Neural Networks 25.8 In Brief 26 Exercises Bibliography Author Index A B C D E F G H J K L M N O P R S T U V W Y Z Subject Index A B C D E F G H I J L M N O P Q R S T U V W X Z

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