### Abstract

polynomial is an adequate approximation (a valid metamodel) of the implicit input/output function of the underlying simulation model; (ii) the directions (signs) of the first-order effects are known (so the first-order polynomial approximation is monotonic); (iii) so-called “heredity” applies; i.e., if an input has no important first-order effect, then this input has no important second-order effects. Moreover—like many other statistical methods—SB assumes Gaussian simulation outputs if the simulation model is stochastic (random). A generalization of SB called “multiresponse SB” (or MSB) uses the same assumptions, but allows for simulation models with multiple types of responses

(outputs). To test whether these assumptions hold, we develop new methods. We evaluate these methods through Monte Carlo experiments and a case study.

Original language | English |
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Place of Publication | Tilburg |

Publisher | CentER, Center for Economic Research |

Number of pages | 27 |

Volume | 2017-006 |

Publication status | Published - 14 Feb 2017 |

### Publication series

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

Volume | 2017-006 |

### Fingerprint

### Keywords

- sensitivity analysis
- experimental desgin
- meta-modeling
- validation
- regression
- simulation

### Cite this

*Testing the Assumptions of Sequential Bifurcation for Factor Screening (revision of CentER DP 2015-034)*. (CentER Discussion Paper; Vol. 2017-006). Tilburg: CentER, Center for Economic Research.

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**Testing the Assumptions of Sequential Bifurcation for Factor Screening (revision of CentER DP 2015-034).** / Shi, Wen; Kleijnen, J.P.C.

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

TY - UNPB

T1 - Testing the Assumptions of Sequential Bifurcation for Factor Screening (revision of CentER DP 2015-034)

AU - Shi, Wen

AU - Kleijnen, J.P.C.

PY - 2017/2/14

Y1 - 2017/2/14

N2 - Sequential bifurcation (or SB) is an efficient and effective factor-screening method; i.e., SB quickly identifies the important factors (inputs) in experiments with simulation models that have very many factors—provided the SB assumptions are valid. The specific SB assumptions are: (i) a secondorderpolynomial is an adequate approximation (a valid metamodel) of the implicit input/output function of the underlying simulation model; (ii) the directions (signs) of the first-order effects are known (so the first-order polynomial approximation is monotonic); (iii) so-called “heredity” applies; i.e., if an input has no important first-order effect, then this input has no important second-order effects. Moreover—like many other statistical methods—SB assumes Gaussian simulation outputs if the simulation model is stochastic (random). A generalization of SB called “multiresponse SB” (or MSB) uses the same assumptions, but allows for simulation models with multiple types of responses(outputs). To test whether these assumptions hold, we develop new methods. We evaluate these methods through Monte Carlo experiments and a case study.

AB - Sequential bifurcation (or SB) is an efficient and effective factor-screening method; i.e., SB quickly identifies the important factors (inputs) in experiments with simulation models that have very many factors—provided the SB assumptions are valid. The specific SB assumptions are: (i) a secondorderpolynomial is an adequate approximation (a valid metamodel) of the implicit input/output function of the underlying simulation model; (ii) the directions (signs) of the first-order effects are known (so the first-order polynomial approximation is monotonic); (iii) so-called “heredity” applies; i.e., if an input has no important first-order effect, then this input has no important second-order effects. Moreover—like many other statistical methods—SB assumes Gaussian simulation outputs if the simulation model is stochastic (random). A generalization of SB called “multiresponse SB” (or MSB) uses the same assumptions, but allows for simulation models with multiple types of responses(outputs). To test whether these assumptions hold, we develop new methods. We evaluate these methods through Monte Carlo experiments and a case study.

KW - sensitivity analysis

KW - experimental desgin

KW - meta-modeling

KW - validation

KW - regression

KW - simulation

M3 - Discussion paper

VL - 2017-006

T3 - CentER Discussion Paper

BT - Testing the Assumptions of Sequential Bifurcation for Factor Screening (revision of CentER DP 2015-034)

PB - CentER, Center for Economic Research

CY - Tilburg

ER -