A note on the choice and the estimation of Kriging models for the analysis of deterministic computer experiments
Our goal in the present article to give an insight on some important questions to be asked when choosing a Kriging model for the analysis of numerical experiments. We are especially concerned about the cases where the size of the design of experiments is relatively small to the algebraic dimension o...
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| Published in | Applied stochastic models in business and industry Vol. 25; no. 2; pp. 115 - 131 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.03.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1524-1904 1526-4025 |
| DOI | 10.1002/asmb.741 |
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| Abstract | Our goal in the present article to give an insight on some important questions to be asked when choosing a Kriging model for the analysis of numerical experiments. We are especially concerned about the cases where the size of the design of experiments is relatively small to the algebraic dimension of the inputs. We first fix the notations and recall some basic properties of Kriging. Then we expose two experimental studies on subjects that are often skipped in the field of computer simulation analysis: the lack of reliability of likelihood maximization with few data and the consequences of a trend misspecification. We finally propose an example from a porous media application, with the introduction of an original Kriging method in which a non‐linear additive model is used as an external trend. Copyright © 2009 John Wiley & Sons, Ltd. |
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| AbstractList | Our goal in the present article to give an insight on some important questions to be asked when choosing a Kriging model for the analysis of numerical experiments. We are especially concerned about the cases where the size of the design of experiments is relatively small to the algebraic dimension of the inputs. We first fix the notations and recall some basic properties of Kriging. Then we expose two experimental studies on subjects that are often skipped in the field of computer simulation analysis: the lack of reliability of likelihood maximization with few data and the consequences of a trend misspecification. We finally propose an example from a porous media application, with the introduction of an original Kriging method in which a non-linear additive model is used as an external trend. Our goal in the present article to give an insight on some important questions to be asked when choosing a Kriging model for the analysis of numerical experiments. We are especially concerned about the cases where the size of the design of experiments is relatively small to the algebraic dimension of the inputs. We first fix the notations and recall some basic properties of Kriging. Then we expose two experimental studies on subjects that are often skipped in the field of computer simulation analysis: the lack of reliability of likelihood maximization with few data and the consequences of a trend misspecification. We finally propose an example from a porous media application, with the introduction of an original Kriging method in which a non‐linear additive model is used as an external trend. Copyright © 2009 John Wiley & Sons, Ltd. |
| Author | Roustant, Olivier Dupuy, Delphine Ginsbourger, David Badea, Anca Carraro, Laurent |
| Author_xml | – sequence: 1 givenname: David surname: Ginsbourger fullname: Ginsbourger, David email: ginsbourger@emse.fr, ginsbourger@gmail.com organization: Département 3MI, Ecole Nationale Supérieure des Mines, 158 cours Fauriel, 42023 Saint-Etienne, France – sequence: 2 givenname: Delphine surname: Dupuy fullname: Dupuy, Delphine organization: Département 3MI, Ecole Nationale Supérieure des Mines, 158 cours Fauriel, 42023 Saint-Etienne, France – sequence: 3 givenname: Anca surname: Badea fullname: Badea, Anca organization: Département 3MI, Ecole Nationale Supérieure des Mines, 158 cours Fauriel, 42023 Saint-Etienne, France – sequence: 4 givenname: Laurent surname: Carraro fullname: Carraro, Laurent organization: Département 3MI, Ecole Nationale Supérieure des Mines, 158 cours Fauriel, 42023 Saint-Etienne, France – sequence: 5 givenname: Olivier surname: Roustant fullname: Roustant, Olivier organization: Département 3MI, Ecole Nationale Supérieure des Mines, 158 cours Fauriel, 42023 Saint-Etienne, France |
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| Cites_doi | 10.1093/biomet/71.1.135 10.1214/ss/1177012413 10.2514/6.2004-4483 10.1007/978-1-4757-3799-8 10.1214/aos/1176345208 10.1137/1.9781611970128 10.1007/978-0-387-21606-5 10.2307/3315678 10.1111/1467-9469.00168 10.2514/1.8650 10.1198/004017004000000671 10.2113/gsecongeo.58.8.1246 10.1007/978-1-4612-1494-6 10.1023/A:1008306431147 10.1016/j.ress.2005.11.025 10.1002/9781119115151 10.1007/BF00893318 |
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| References | Li R, Sudjianto A. Analysis of computer experiments using penalized likelihood in gaussian Kriging models. Technometrics 2005; 47:111-120. O'Hagan A. Bayesian analysis of computer code outputs: a tutorial. Reliability Engineering and System Safety 2006; 91:1290-1300. Sweeting TJ. Uniform asymptotic normality of the maximum likelihood estimator. The Annals of Statistics 1980; 8(6):1375-1381. Abt M. Estimating the prediction mean squared error in gaussian stochastic processes with exponential covariance structure. Scandinavian Journal of Statistics 1999; 26:563-578. Jourdan A. Approches statistiques des expériences simulées. Revue de Statistiques Appliquées 2002; 50:49-64. Wahba G. Spline Models for Observational Data. SIAM: Philadelphia, PA, 1990. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2006. ISBN 3-900051-07-0. Hastie T, Tibshirani R. Generalized Additive Models. Chapman & Hall: London, 1991. Abt M, Welch WJ. Fisher information and maximum-likelihood estimation of covariance parameters in gaussian stochastic processes. The Canadian Journal of Statistics 1998; 26:127-137. Journel AG, Rossi ME. When do we need a trend model in Kriging? Mathematical Geology 1989; 21(7):715-739. Mardia KV, Marshall RJ. Maximum likelihood estimation of models for residual covariance in spatial regression. Biometrika 1984; 71:135-146. Santner TJ, Williams BJ, Notz WJ. The Design and Analysis of Computer Experiments. Springer: Berlin, 2003. Sacks J, Welch WJ, Mitchell TJ, Wynn HP. Design and analysis of computer experiments. Statistical Science 1989; 4(4):409-435. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer: Berlin, 2001. Stein ML. Interpolation of Spatial Data, Some Theory for Kriging. Springer: Berlin, 1999. Martin JD, Simpson TW. Use of Kriging models to approximate deterministic computer models. AIAA Journal 2005; 43(4):853-863. Matheron G. Principles of geostatistics. Economic Geology 1963; 58:1246-1266. Jones DR, Schonlau M, Welch WJ. Efficient global optimization of expensive black-box functions. Journal of Global Optimization 1998; 13:455-492. 1963; 58 1998; 26 1989; 4 2006; 91 1984; 71 1989; 21 2001 1990 2002; 50 1999; 26 1980; 8 1996 2005; 43 2006 1993 2004 2003 1991 1998; 13 2005; 47 1999 Hastie T (e_1_2_1_20_2) 1991 Jourdan A (e_1_2_1_7_2) 2002; 50 R Development Core Team (e_1_2_1_22_2) 2006 e_1_2_1_6_2 e_1_2_1_4_2 e_1_2_1_5_2 e_1_2_1_2_2 e_1_2_1_11_2 e_1_2_1_3_2 e_1_2_1_12_2 e_1_2_1_23_2 e_1_2_1_10_2 e_1_2_1_21_2 e_1_2_1_15_2 e_1_2_1_16_2 e_1_2_1_13_2 e_1_2_1_24_2 e_1_2_1_14_2 e_1_2_1_25_2 e_1_2_1_19_2 e_1_2_1_8_2 e_1_2_1_17_2 e_1_2_1_9_2 e_1_2_1_18_2 |
| References_xml | – reference: Abt M. Estimating the prediction mean squared error in gaussian stochastic processes with exponential covariance structure. Scandinavian Journal of Statistics 1999; 26:563-578. – reference: Li R, Sudjianto A. Analysis of computer experiments using penalized likelihood in gaussian Kriging models. Technometrics 2005; 47:111-120. – reference: O'Hagan A. Bayesian analysis of computer code outputs: a tutorial. Reliability Engineering and System Safety 2006; 91:1290-1300. – reference: Abt M, Welch WJ. Fisher information and maximum-likelihood estimation of covariance parameters in gaussian stochastic processes. The Canadian Journal of Statistics 1998; 26:127-137. – reference: Wahba G. Spline Models for Observational Data. SIAM: Philadelphia, PA, 1990. – reference: Sacks J, Welch WJ, Mitchell TJ, Wynn HP. Design and analysis of computer experiments. Statistical Science 1989; 4(4):409-435. – reference: Matheron G. Principles of geostatistics. Economic Geology 1963; 58:1246-1266. – reference: Santner TJ, Williams BJ, Notz WJ. The Design and Analysis of Computer Experiments. Springer: Berlin, 2003. – reference: Sweeting TJ. Uniform asymptotic normality of the maximum likelihood estimator. The Annals of Statistics 1980; 8(6):1375-1381. – reference: Journel AG, Rossi ME. When do we need a trend model in Kriging? Mathematical Geology 1989; 21(7):715-739. – reference: Hastie T, Tibshirani R. Generalized Additive Models. Chapman & Hall: London, 1991. – reference: Jourdan A. Approches statistiques des expériences simulées. Revue de Statistiques Appliquées 2002; 50:49-64. – reference: Mardia KV, Marshall RJ. Maximum likelihood estimation of models for residual covariance in spatial regression. Biometrika 1984; 71:135-146. – reference: Martin JD, Simpson TW. Use of Kriging models to approximate deterministic computer models. AIAA Journal 2005; 43(4):853-863. – reference: Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer: Berlin, 2001. – reference: Stein ML. Interpolation of Spatial Data, Some Theory for Kriging. Springer: Berlin, 1999. – reference: R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2006. ISBN 3-900051-07-0. – reference: Jones DR, Schonlau M, Welch WJ. Efficient global optimization of expensive black-box functions. Journal of Global Optimization 1998; 13:455-492. – volume: 13 start-page: 455 year: 1998 end-page: 492 article-title: Efficient global optimization of expensive black‐box functions publication-title: Journal of Global Optimization – volume: 43 start-page: 853 issue: 4 year: 2005 end-page: 863 article-title: Use of Kriging models to approximate deterministic computer models publication-title: AIAA Journal – volume: 21 start-page: 715 issue: 7 year: 1989 end-page: 739 article-title: When do we need a trend model in Kriging? publication-title: Mathematical Geology – volume: 26 start-page: 563 year: 1999 end-page: 578 article-title: Estimating the prediction mean squared error in gaussian stochastic processes with exponential covariance structure publication-title: Scandinavian Journal of Statistics – start-page: 4483 – volume: 71 start-page: 135 year: 1984 end-page: 146 article-title: Maximum likelihood estimation of models for residual covariance in spatial regression publication-title: Biometrika – year: 2001 – year: 2006 – year: 2003 – year: 2004 – year: 1996 – volume: 26 start-page: 127 year: 1998 end-page: 137 article-title: Fisher information and maximum‐likelihood estimation of covariance parameters in gaussian stochastic processes publication-title: The Canadian Journal of Statistics – volume: 47 start-page: 111 year: 2005 end-page: 120 article-title: Analysis of computer experiments using penalized likelihood in gaussian Kriging models publication-title: Technometrics – volume: 91 start-page: 1290 year: 2006 end-page: 1300 article-title: Bayesian analysis of computer code outputs: a tutorial publication-title: Reliability Engineering and System Safety – volume: 4 start-page: 409 issue: 4 year: 1989 end-page: 435 article-title: Design and analysis of computer experiments publication-title: Statistical Science – volume: 50 start-page: 49 year: 2002 end-page: 64 article-title: Approches statistiques des expériences simulées publication-title: Revue de Statistiques Appliquées – year: 1991 – volume: 8 start-page: 1375 issue: 6 year: 1980 end-page: 1381 article-title: Uniform asymptotic normality of the maximum likelihood estimator publication-title: The Annals of Statistics – year: 1990 – year: 1993 – volume: 58 start-page: 1246 year: 1963 end-page: 1266 article-title: Principles of geostatistics publication-title: Economic Geology – year: 1999 – ident: e_1_2_1_12_2 doi: 10.1093/biomet/71.1.135 – ident: e_1_2_1_14_2 – ident: e_1_2_1_4_2 doi: 10.1214/ss/1177012413 – ident: e_1_2_1_9_2 doi: 10.2514/6.2004-4483 – ident: e_1_2_1_25_2 – ident: e_1_2_1_6_2 doi: 10.1007/978-1-4757-3799-8 – ident: e_1_2_1_11_2 doi: 10.1214/aos/1176345208 – ident: e_1_2_1_21_2 doi: 10.1137/1.9781611970128 – ident: e_1_2_1_17_2 doi: 10.1007/978-0-387-21606-5 – ident: e_1_2_1_24_2 – ident: e_1_2_1_13_2 doi: 10.2307/3315678 – volume: 50 start-page: 49 year: 2002 ident: e_1_2_1_7_2 article-title: Approches statistiques des expériences simulées publication-title: Revue de Statistiques Appliquées – ident: e_1_2_1_15_2 doi: 10.1111/1467-9469.00168 – ident: e_1_2_1_8_2 doi: 10.2514/1.8650 – ident: e_1_2_1_16_2 doi: 10.1198/004017004000000671 – ident: e_1_2_1_23_2 – ident: e_1_2_1_2_2 doi: 10.2113/gsecongeo.58.8.1246 – ident: e_1_2_1_18_2 doi: 10.1007/978-1-4612-1494-6 – ident: e_1_2_1_5_2 doi: 10.1023/A:1008306431147 – ident: e_1_2_1_10_2 doi: 10.1016/j.ress.2005.11.025 – volume-title: Generalized Additive Models year: 1991 ident: e_1_2_1_20_2 – volume-title: R: A Language and Environment for Statistical Computing year: 2006 ident: e_1_2_1_22_2 – ident: e_1_2_1_3_2 doi: 10.1002/9781119115151 – ident: e_1_2_1_19_2 doi: 10.1007/BF00893318 |
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| Title | A note on the choice and the estimation of Kriging models for the analysis of deterministic computer experiments |
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