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                    | Santner T.J., Williams B.J., Notz W.I. — The Design and Analysis of Computer Experiments | 
                  
                
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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,  -robust      21  
Robustness criteria,  -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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