### Abstract

Original language | English |
---|---|

Place of Publication | Tilburg |

Publisher | Operations research |

Number of pages | 36 |

Volume | 1997-116 |

Publication status | Published - 1997 |

### Publication series

Name | CentER Discussion Paper |
---|---|

Volume | 1997-116 |

### Fingerprint

### Keywords

- Simulation
- approximation
- response surface
- modelling
- regression

### Cite this

*A Methodology for Fitting and Validating Metamodels in Simulation*. (CentER Discussion Paper; Vol. 1997-116). Tilburg: Operations research.

}

**A Methodology for Fitting and Validating Metamodels in Simulation.** / Kleijnen, J.P.C.; Sargent, R.

Research output: Working paper › Discussion paper › Other research output

TY - UNPB

T1 - A Methodology for Fitting and Validating Metamodels in Simulation

AU - Kleijnen, J.P.C.

AU - Sargent, R.

N1 - Pagination: 36

PY - 1997

Y1 - 1997

N2 - This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels.

AB - This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels.

KW - Simulation

KW - approximation

KW - response surface

KW - modelling

KW - regression

M3 - Discussion paper

VL - 1997-116

T3 - CentER Discussion Paper

BT - A Methodology for Fitting and Validating Metamodels in Simulation

PB - Operations research

CY - Tilburg

ER -