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E-grāmata: Design and Modeling for Computer Experiments

(Pennsylvania State University, University Park, USA), (Bank of America, Charlotte, North Carolina, USA), (Hong Kong Baptist University, Kowloon)
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Computer simulations based on mathematical models have become ubiquitous across the engineering disciplines and throughout the physical sciences. Successful use of a simulation model, however, requires careful interrogation of the model through systematic computer experiments. While specific theoretical/mathematical examinations of computer experiment design are available, those interested in applying proposed methodologies need a practical presentation and straightforward guidance on analyzing and interpreting experiment results.

Written by authors with strong academic reputations and real-world practical experience, Design and Modeling for Computer Experiments is exactly the kind of treatment you need. The authors blend a sound, modern statistical approach with extensive engineering applications and clearly delineate the steps required to successfully model a problem and provide an analysis that will help find the solution. Part I introduces the design and modeling of computer experiments and the basic concepts used throughout the book. Part II focuses on the design of computer experiments. The authors present the most popular space-filling designs - like Latin hypercube sampling and its modifications and uniform design - including their definitions, properties, construction and related generating algorithms. Part III discusses the modeling of data from computer experiments. Here the authors present various modeling techniques and discuss model interpretation, including sensitivity analysis. An appendix reviews the statistics and mathematics concepts needed, and numerous examples clarify the techniques and their implementation.

The complexity of real physical systems means that there is usually no simple analytic formula that sufficiently describes the phenomena. Useful both as a textbook and professional reference, this book presents the techniques you need to design and model computer experiments for practical problem solving.

Recenzijas

". . . very well-organized text . . . makes a very valuable contribution to the field. I highly recommend it for anyone trying to learn design and modeling techniques for computer experiments. In particular, it will be a useful professional reference for scientists and engineers in practicing computer experiments, a comprehensive resource book for statisticians interested in developing new techniques for designing and modeling computation experiments, and an excellent book for undergraduate and graduate students. The authors careful and thorough presentation style makes the book a very enjoyable read."

Hao Helen Zhang, North Carolina State University, in JASA, December 2008

Part I An Overview 1(44)
1 Introduction
3(42)
1.1 Experiments and Their Statistical Designs
3(1)
1.2 Some Concepts in Experimental Design
4(6)
1.3 Computer Experiments
10(10)
1.3.1 Motivations
10(2)
1.3.2 Metamodels
12(4)
1.3.3 Computer Experiments in Engineering
16(4)
1.4 Examples of Computer Experiments
20(4)
1.5 Space-Filling Designs
24(2)
1.6 Modeling Techniques
26(5)
1.7 Sensitivity Analysis
31(2)
1.8 Strategies for Computer Experiments and an Illustration Case Study
33(5)
1.9 Remarks on Computer Experiments
38(2)
1.10 Guidance for Reading This Book
40(5)
Part II Designs for Computer Experiments 45(80)
2 Latin Hypercube Sampling and Its Modifications
47(20)
2.1 Latin Hypercube Sampling
47(4)
2.2 Randomized Orthogonal Array
51(3)
2.3 Symmetric and Orthogonal Column Latin Hypercubes
54(6)
2.4 Optimal Latin Hypercube Designs
60(7)
2.4.1 IMSE Criterion
60(2)
2.4.2 Entropy Criterion
62(2)
2.4.3 Minimax and Maximin Distance Criteria and Their extension
64(1)
2.4.4 Uniformity Criterion
65(2)
3 Uniform Experimental Design
67(38)
3.1 Introduction
67(1)
3.2 Measures of Uniformity
68(10)
3.2.1 The Star Lp-Discrepancy
68(2)
3.2.2 Modified L2-Discrepancy
70(1)
3.2.3 The Centered Discrepancy
71(1)
3.2.4 The Wrap-Around Discrepancy
72(1)
3.2.5 A Unified Definition of Discrepancy
73(2)
3.2.6 Descrepancy for Categorical Factors
75(1)
3.2.7 Applications of Uniformity in Experimental Designs
76(2)
3.3 Construction of Uniform Designs
78(12)
3.3.1 One-Factor Uniform Designs
78(1)
3.3.2 Symmetrical Uniform Designs
79(1)
3.3.3 Good Lattice Point Method
80(5)
3.3.4 Latin Square Method
85(1)
3.3.5 Expanding Orthogonal Array Method
86(1)
3.3.6 The Cutting Method
86(4)
3.3.7 Construction of Uniform Designs by Optimization
90(1)
3.4 Characteristics of the Uniform Design: Admissibility, Minimaxity, and Robustness
90(3)
3.5 Construction of Uniform Designs via Resolvable Balanced Incomplete Block Designs
93(4)
3.5.1 Resolvable Balanced Incomplete Block Designs
93(1)
3.5.2 RBIBD Construction Method
94(1)
3.5.3 New Uniform Designs
94(3)
3.6 Construction of Asymmetrical Uniform Designs
97(8)
3.6.1 Pseudo-Level Technique
97(1)
3.6.2 Collapsing Method
97(3)
3.6.3 Combinatorial Method
100(3)
3.6.4 Miscellanea
103(2)
4 Optimization in Construction of Designs for Computer Experiments
105(20)
4.1 Optimization Problem in Construction of Designs
105(8)
4.1.1 Algorithmic Construction
106(1)
4.1.2 Neighborhood
106(1)
4.1.3 Replacement Rule
107(2)
4.1.4 Iteration Formulae
109(4)
4.2 Optimization Algorithms
113(4)
4.2.1 Algorithms
113(1)
4.2.2 Local Search Algorithm
114(1)
4.2.3 Simulated Annealing Algorithm
115(1)
4.2.4 Threshold Accepting Algorithm
115(1)
4.2.5 Stochastic Evolutionary Algorithm
116(1)
4.3 Lower Bounds of the Discrepancy and Related Algorithm
117(10)
4.3.1 Lower Bounds of the Categorical Discrepancy
119(1)
4.3.2 Lower Bounds of the Wrap-Around L2-Discrepancy
119(2)
4.3.3 Lower Bounds of the Centered L2-Discrepancy
121(1)
4.3.4 Balance-Pursuit Heuristic Algorithm
122(3)
Part III Modeling for Computer Experiments 125(116)
5 Metamodeling
127(60)
5.1 Basic Concepts
127(6)
5.1.1 Mean Square Error and Prediction Error
127(3)
5.1.2 Regularization
130(3)
5.2 Polynomial Models
133(6)
5.3 Spline Method
139(6)
5.3.1 Construction of Spline Basis
140(2)
5.3.2 An Illustration
142(2)
5.3.3 Other Bases of Global Approximation
144(1)
5.4 Gaussian Kriging Models
145(14)
5.4.1 Prediction via Kriging
146(1)
5.4.2 Estimation of Parameters
147(6)
5.4.3 A Case Study
153(6)
5.5 Bayesian Approach
159(8)
5.5.1 Gaussian Processes
159(1)
5.5.2 Bayesian Prediction of Deterministic Functions
160(2)
5.5.3 Use of Derivatives in Surface Prediction
162(3)
5.5.4 An Example: Borehole Model
165(2)
5.6 Neural Network
167(13)
5.6.1 Multi-Layer Perceptron Networks
168(4)
5.6.2 A Case Study
172(5)
5.6.3 Radial Basis Functions
177(3)
5.7 Local Polynomial Regression
180(4)
5.7.1 Motivation of Local Polynomial Regression
180(3)
5.7.2 Metamodeling via Local Polynomial Regression
183(1)
5.8 Some Recommendations
184(3)
5.8.1 Connections
184(1)
5.8.2 Recommendations
185(2)
6 Model Interpretation
187(20)
6.1 Introduction
187(1)
6.2 Sensitivity Analysis Based on Regression Analysis
188(5)
6.2.1 Criteria
188(3)
6.2.2 An Example
191(2)
6.3 Sensitivity Analysis Based on Variation Decomposition
193(14)
6.3.1 Functional ANOVA Representation
193(2)
6.3.2 Computational Issues
195(3)
6.3.3 Example of Sobol' Global Sensitivity
198(1)
6.3.4 Correlation Ratios and Extension of Sobol' Indices
199(3)
6.3.5 Fourier Amplitude Sensitivity Test
202(3)
6.3.6 Example of FAST Application
205(2)
7 Functional Response
207(34)
7.1 Computer Experiments with Functional Response
207(8)
7.2 Spatial Temporal Models
215(4)
7.2.1 Functional Response with Sparse Sampling Rate
215(3)
7.2.2 Functional Response with Intensive Sampling Rate
218(1)
7.3 Penalized Regression Splines
219(3)
7.4 Functional Linear Models
222(8)
7.4.1 A Graphical Tool
223(1)
7.4.2 Efficient Estimation Procedure
224(2)
7.4.3 An Illustration
226(4)
7.5 Semiparametric Regression Models
230(14)
7.5.1 Partially Linear Model
230(4)
7.5.2 Partially Functional Linear Models
234(2)
7.5.3 An Illustration
236(5)
Appendix 241(20)
A.1 Some Basic Concepts in Matrix Algebra
241(3)
A.2 Some Concepts in Probability and Statistics
244(5)
A.2.1 Random Variables and Random Vectors
244(3)
A.2.2 Some Statistical Distributions and Gaussian Process
247(2)
A.3 Linear Regression Analysis
249(7)
A.3.1 Linear Models
250(1)
A.3.2 Method of Least Squares
251(1)
A.3.3 Analysis of Variance
252(1)
A.3.4 An Illustration
253(3)
A.4 Variable Selection for Linear Regression Models
256(5)
A.4.1 Nonconvex Penalized Least Squares
257(1)
A.4.2 Iteratively Ridge Regression Algorithm
258(1)
A.4.3 An Illustration
259(2)
Acronyms 261(2)
References 263(20)
Index 283(4)
Author Index 287
Kai-Tai Fang, Runze Li, Agus Sudjianto