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Sensitivity Analysis of Simulation Models

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  • Kleijnen, J.P.C.

    (Tilburg University, Center for Economic Research)

Abstract

This contribution presents an overview of sensitivity analysis of simulation models, including the estimation of gradients. It covers classic designs and their corresponding (meta)models; namely, resolution-III designs including fractional-factorial two-level designs for first-order polynomial metamodels, resolution-IV and resolution-V designs for metamodels augmented with two-factor interactions, and designs for second-degree polynomial metamodels including central composite designs. It also reviews factor screening for simulation models with very many factors, focusing on the so-called "sequential bifurcation" method. Furthermore, it reviews Kriging metamodels and their designs. It mentions that sensitivity analysis may also aim at the optimization of the simulated system, allowing multiple random simulation outputs.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2009-11.

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Date of creation: 2009
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Handle: RePEc:dgr:kubcen:200911

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Web page: http://center.uvt.nl

Related research

Keywords: simulation; sensitivity analysis; gradients; screening; Kriging; optimization; Response SurfaceMethodology; Taguchi;

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Cited by:
  1. Kleijnen, J.P.C., 2006. "White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice," Discussion Paper 2006-50, Tilburg University, Center for Economic Research.
  2. Kleijnen, J.P.C., 1997. "Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models," Discussion Paper 1997-52, Tilburg University, Center for Economic Research.
  3. Kleijnen, Jack P.C. & Mehdad, E., 2012. "Kriging in Multi-response Simulation, including a Monte Carlo Laboratory (Replaced by 2014-012)," Discussion Paper 2012-039, Tilburg University, Center for Economic Research.
  4. Kleijnen, J.P.C., 1995. "Sensitivity analysis and related analysis: A survey of statistical techniques," Research Memorandum 706, Tilburg University, Faculty of Economics and Business Administration.

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