A note on the choice and the estimation of Kriging models for the analysis of deterministic computer experiments
| Autor: | Delphine Dupuy, Anca Badea, David Ginsbourger, Olivier Roustant, Laurent Carraro |
|---|---|
| Přispěvatelé: | Ginsbourger, David, Département Méthodes et Modèles Mathématiques pour l'Industrie (3MI-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre G2I, Consortium DICE |
| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: |
Maximum Likelihood
Computer science 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research [STAT.OT]Statistics [stat]/Other Statistics [stat.ML] computer.software_genre Metamodeling 01 natural sciences Field (computer science) Deter- minisitic Drift 010104 statistics & probability Kriging 0101 mathematics Dimension (data warehouse) Additive model Reliability (statistics) 021103 operations research Design of experiments Computer experiment General Business Management and Accounting [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [STAT.OT] Statistics [stat]/Other Statistics [stat.ML] Modeling and Simulation Data mining [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation Additive Models computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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