Convex and Monotonic Bootstrapped Kriging
AbstractAbstract: Distribution-free bootstrapping of the replicated responses of a given discreteevent simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kriging, we use an M/M/1 queueing simulation with as output either the mean or the 90% quantile of the transient-state waiting times, and as input the traffic rate. In this example, monotonic bootstrapped Kriging enables better sensitivity analysis than classic Kriging; i.e., bootstrapping gives lower MSE and confidence intervals with higher coverage and the same length. To illustrate convex Kriging, we start with simulationoptimization of an (s, S) inventory model, but we next switch to a Monte Carlo experiment with a second-order polynomial inspired by this inventory simulation. We could not find truly convex Kriging metamodels, either classic or bootstrapped; nevertheless, our bootstrapped "nearly convex" Kriging does give a confidence interval for the optimal input combination.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2012-066.
Date of creation: 2012
Date of revision:
Contact details of provider:
Web page: http://center.uvt.nl
Distribution-free bootstrapping; Gaussian process; random simulation; sensitivity analysis; optimization; confidence intervals;
Other versions of this item:
- C0 - Mathematical and Quantitative Methods - - General
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C9 - Mathematical and Quantitative Methods - - Design of Experiments
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kleijnen, J.P.C., 2008. "Review of the book [Design and Analysis of Simulation Experiments]," Open Access publications from Tilburg University urn:nbn:nl:ui:12-4379049, Tilburg University.
- Kleijnen, Jack P.C. & Beers, W.C.M. van, 2013.
"Monotonicity-preserving bootstrapped kriging metamodels for expensive simulations,"
Open Access publications from Tilburg University
urn:nbn:nl:ui:12-5904627, Tilburg University.
- J P C Kleijnen & W C M van Beers, 2013. "Monotonicity-preserving bootstrapped Kriging metamodels for expensive simulations," Journal of the Operational Research Society, Palgrave Macmillan, vol. 64(5), pages 708-717, May.
- Kleijnen, Jack P.C. & Beers, W.C.M. van, 2009. "Monotonicity-Preserving Bootstrapped Kriging Metamodels for Expensive Simulations," Discussion Paper 2009-75, Tilburg University, Center for Economic Research.
- Kleijnen, Jack P.C., 2013. "Simulation-Optimization via Kriging and Bootstrapping: A Survey (Revision of CentER DP 2011-064)," Discussion Paper 2013-064, Tilburg University, Center for Economic Research.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Richard Broekman).
If references are entirely missing, you can add them using this form.