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