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Santner T.J., Williams B.J., Notz W.I. — The Design and Analysis of Computer Experiments
Santner T.J., Williams B.J., Notz W.I. — The Design and Analysis of Computer Experiments



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Название: The Design and Analysis of Computer Experiments

Авторы: Santner T.J., Williams B.J., Notz W.I.

Аннотация:

This book describes methods for designing and analyzing experiments that are conducted using a computer code rather than a physical experiment. It discusses how to select the values of the factors at which to run the code (the design of the computer experiment) in light of the research objectives of the experimenter. It also provides techniques for analyzing the resulting data so as to achieve these research goals. It illustrates these methods with code that is available to the reader at the companion Web site for the book.


Язык: en

Рубрика: Computer science/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2003

Количество страниц: 283

Добавлена в каталог: 09.12.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
Lim, Y.B.      261
Lin, C.H.      199 200 202 252
Lin, D.K.      143 145 148 150 256
Lindley, D.V.      165 261
Linn, R.      4 9 200 252
Liu, L.      18 61 130 172 173 252
Local improvement      175 176 184
Lock bead distance      9
Loh, W.-L.      18 261
Low-discrepancy sequence      146
Lucas, T.      256
Lucas, T.W.      260
Lynn, R.R.      4 262
Main effect      134 194
Markov chain Monte Carlo      69
Martz, F.      24 201 202 266
Matern, B.      41 261
Matheron, G.      24 262
Maximin distance design      see "Designs"
Maximum entropy design      see "Designs"
Maximum entropy sampling      see "Sampling"
Maximum likelihood      see "Estimation methods"
Maximum likelihood EBLUP      see "Predictors"
Maximum mean squared prediction error      170
McCurley, R.D.      258
McDonald, G.C.      262 268
McKay, M.D.      18 132 158 262
Mead, R.      85 263
Mean squared continuous      32
Mean squared prediction error      52 92 168
Meiring, W.      31 257
Mejia, J.M.      41 266
Mendes, B.      256
mesh      174
Minimax distance design      see "Designs"
Minimum efficiency      see "Designs"
Minimum index      149
Minimum linear MSPE predictor      see "Predictors: best linear MSPE predictor"
Minimum linear unbiased MSPE predictor      see "Predictors: best linear unbiased MSPE predictor"
Minimum MSPE predictor      see "Predictors: best MSPE predictor"
Mitchell, T.J.      22 35 37 66 85 92 104 130 150 167 193 195 255 262 263 266 270
MLE      see "Estimation methods: maximum likelihood"
MLE-EBLUP      see "Predictors"
Mockus, A.      18 24 177 257 262 263
Mockus, J.      18 24 176 177 262 263
Mockus, L.      18 24 177 262 263
Modified Bessel function      see "Bessel function"
Monotonic function and LHD      148
Monte Carlo      69 183
Montgomery, P.      7 8 9 263
Moore, L.M.      139 149 167 260
Moriarity, N.W.      268
Morris, M.D.      22 35 37 66 85 104 130 150 167 193 195 255 262 263 270
Moving average stochastic model      103
Moya, J.      200 201 265
Mrawira, D.      263
MSPE      see "Mean squared prediction error"
Mulder, R.      269
Multiple output model      22—23
Multivaiiate Student t distribution      see "Distributions"
Multivariate normal distribution      see "Distributions"
Naylor, J.C.      263
Nazaret, W.A.      18 61 130 172 173 252
Neal, R.      263
Neal, R.M.      69 263
Nelder — Mead algorithm      see "Optimization algorithms"
Nelder, J.A.      85 263
Neural network      62
Niederreiter, H.      143 144 145 159 160 253 264
Non-central Student t distribution      see "Distributions"
Non-informative      see "Prior"
Nonmonotone effects      192
Nonredundancy in designs      139 140
Nonsingular      210
Nonstationary      31
Notz, W.I.      6 7 10 16 19 22 84 182 184 185 186 199 200 254 261 270 271
Nugget effect      103
Numerical integration and design      159
O'Hagan, A.      16 18 22 102 109 130 200 202 258 260 264
Oakley, J.E.      16 18 24 46 264
Oberkampf, W.L.      200 269
Omre, H.      264
Optimization algorithms, branch and bound      85
Optimization algorithms, generalized pattern search      174
Optimization algorithms, Nelder — Mead simplex      85
Ordinary least squares estimate      58 61
Ornstein — Uhlenbeck process      36
Orthogonal array      see "Designs"
Owen, A.B.      18 86 134 135 136 150 158 159 160 251 261 264
Padula, A.D.      69 71 269
Palmer, K.      150 265
Parametric empirical kriging      215—249
Park, B.      4 9 200 252 266
Park, J.S.      18 200 254 265 267
Partial correlation coefficient      191
Partial derivative      22 39 104
Partial derivative process      25 39 104
Partial variance      194 195
Parzen, E.      104 265
Patterson, H.D.      66 265
Paulo, R.      199 200 202 252
PCC      see "Partial correlation coefficient"
Pebesma, E.J.      18 265
Peercy, D.      200 201 265
Pereira, A.      256
Perelson, A.      4 9 200 252
PErK      see "Parametric empirical kriging"
Piepel, G.F.      265
Pilch, M.      200 201 265 269
Pointwise prediction interval      94 96
Poll step      175 176
Poole, D.      265
Porostosky, J.      271
Positive basis      174
Posterior distribution      68 98 99 166 179 183
Posterior mean      89—92
Posterior mode      see "Estimation methods"
Posterior mode EBLUP      see "Predictors"
Power parameter      65
Prado, P.      256
Prasad, N.G.N.      98 265
Prediction band      see "Pointwise prediction interval"
Prediction bounds      see "Pointwise prediction interval"
Prediction interval      see "Pointwise prediction interval"
Predictive accuracy      69—76
Predictive distribution      87—88
Predictors, best linear MSPE      59
Predictors, best linear unbiased MSPE      59 60
Predictors, best MSPE      51
Predictors, cross-validation EBLUP      68
Predictors, empirical best linear unbiased      65
Predictors, linear      50
Predictors, linear unbiased      50
Predictors, MLE-EBLUP      66
Predictors, posterior mode EBLUP      68—69
Predictors, regression      50 70
Predictors, REML-EBLUP      66—68
Predictors, unbiased      50
Prior distribution, improper      53
Prior distribution, informative      88 89 94 96
Prior distribution, Jeffreys prior      94
Prior distribution, non-informative      47 88—90 94 96
Projection properties of a design      127 141
Proportional sampling      see "Sampling"
Prosthesis      6 15 19 199
Pukelsheim, F.      121 123 171 265
Punch plan      8
Quasi-regression      86
Rabinowitz, M.J.      256
Raftery, A.E.      258 265 266
Raghavan, N.      266
Raghavarao, D.      122 135 266
Rank transformation      192
Rao, J.N.K.      98 265
Reese, C.S.      24 201 202 266
Reklaitis, G.      18 24 263
REML      see "Estimation methods: restricted maximum likelihood"
REML-EBLUP      see "Predictors"
Residual maximum likelihood estimation      see "Estimation methods: restricted maximum likelihood"
Riccomagno, E.      159 252
Rinnooy Kan, A.H.G.      85 266
Ripley, B.D.      24 266
Roache, P.J.      201 266
Robert, C.P.      69 266
Robustness criteria, $\mathcal{G}$-robust      21
Robustness criteria, $\pi(\cdot)$-robust      21
Robustness criteria, Bayes      21
Robustness criteria, M-robust      21
Robustness criteria, minimax      20
Robustness criteria, Taguchi      21
Robustness criteria, V-robust      22
Rodriguez-Iturbe, I.      41 266
Romano, D.      266
Rougier, J.C.      18 200 202 254 257
Rouphail, N.      4 9 200 252
Rouphail, N.M.      200 266
Rudeen, D.K.      258
Ryan, K.      24 201 202 266
Sacks, J.      4 9 18 61 66 85 92 130 167 168 169 170 172 173 193 195 199 200 202 251 252 261 266 270
Sahama, A.R.      4 267
Saltelli, A.      189 190 193 195 197 198 256 259 267
Sampling, Latin hypercube      127—132
Sampling, maximum entropy      166
Sampling, proportional      133 152 158
Sampling, simple random      126
Sampling, stratified random      126
Sampson, P.D.      31 257 267
Sanchez, S.M.      260
Sanso, B.      46 69 252
Santner, T.J.      6 7 10 16 19 22 84 182 184 185 186 199 200 254 261 270 271
Schiller, S.B.      167 168 169 170 266
Schlensinger, M.E.      11 261
Schmuland, B.      258
Schoenberg, F.      4 9 200 252
Schonlau, M.      18 85 173 178 181 182 183 184 185 260 263 267
Scott, D.S.      167 262
Scott, E.      189 190 193 195 267
Search step      175 176
Searle, S.R.      267
Second-order stationary      30
Seheult, A.H.      18 200 202 254 255
Sensitivity analysis      189—199
Sensitivity analysis, first-order sensitivity index      194
Sensitivity analysis, higher-order sensitivity index      194
Sensitivity analysis, interaction plot      194
Sensitivity analysis, main effect plot      194
Sensitivity analysis, second-order sensitivity index      194
Separable      28
Sequential design      see "Designs: global optimization"
Serafini, D.B.      10 61 174 175 176 253
Shannon, C.E.      165 267
Shewry, M.C.      166 167 267
Shoemaker, A.C.      267
Silvey, S.D.      121 123 267
Sim, J.W.      267
Simple random sampling      see "Sampling"
Singer, C.E.      18 254
Skorokhod, A.V.      257
Slice sampling      69
Sloane, N.      122 258
SMF      see "Surrogate management framework"
Smith, A.F.M.      263 270
Smith, H.      192 256
Smith, J.A.      254 255
Sobol' sequence      see "Designs: other criteria"
Sobol', I.M.      193 267
Space-filling designs      see "Designs"
Spatial autoregressive model      102
Spectral density      33 34
SPLINE      37 62
SRC      see "Standardized regression coefficient"
Standardized regression coefficient      191
Stationarity      29
Stegun, I.      251
Stein, M.L.      18 33 35 37 41 99 106 127 134 135 217 257 268
Steinberg, D.M.      253 268
Stone, M.      67 268
Stopping criterion      181 184
Stratified random sampling      see "Sampling"
Street, A.P.      122 268
Street, D.J.      122 268
Strength of an orthogonal array      136
Stroup, D.W.      4 254
Studden, W.J.      261 268
Stufken, J.      122 258
Swall, J.      31 259
Szego, G.P.      255
Takemura, A.      259
Tang, B.      18 137 150 268
Tarantola, S.      256
Thakuriah, P.      200 266
Thompson, R.      66 265
Tibshirani, R.      24 67 258
Tiesis, V.      18 176 177 263
Timmer, G.T.      85 266
Toda, M.D.      252
Tong, Y.L.      212 268
Tonse, S.R.      268
Torczon, V.      10 61 174 175 176 253 268
Total variance      194
Trosset, M.W.      61 69 71 150 174 175 176 253 255 268 269
Trucano, T.G.      199 200 201 259 265 269
Truss, L.T.      7 8 9 263
Tsui, K.-L.      150 265 267
Tu, J.      199 200 202 252
Tuning parameter      16
Two-factor interaction      191 193 195
Unbiased estimator      132 153
Unconstrained optimization      see "Designs: global optimization"
Uniform designs      see "Designs"
Validation      199—203
van Beers, W.C.M.      260
van Casteren, P.      269
van dei Vaart, A.      269
Variables, active      176
Variables, control      15
Variables, engineering      see "Control"
Variables, environmental      16
Variables, manufacturing      see "Control"
variables, noise      see "Environmental"
Variogram      99
Vecchia, A.V.      35 41 269
Ver Hoef, J.M.      103 252 269
Verification      201
Vicario, G.      266
Voss, D.      121 255
Wahba, G.      68 257 260 270
Walker, S.G.      270
Wallis, J.R.      41 46 257
Walton, W.      4 270
Wang, H.      256
Welch, W.J.      18 61 66 85 92 130 140 150 167 168 169 170 172 173 178 181 182 183 184 185 193 195 225 251 252 257 260 261 263 266 267 270
White noise      31 40 41 58 216 218
Wiens, D.P.      144 148 258 270
Williams, B.J.      6 7 10 16 19 66 84 182 184 185 186 199 200 254 270 271
Wilson, A.G.      24 201 202 266
Winker, P.      143 145 148 150 256
Wolpert, R.L.      271
Wonderland model      11
Wu, C.F.J.      121 135 267 271
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