DocumentCode
1816725
Title
Better simulation metamodeling: The why, what, and how of stochastic kriging
Author
Staum, Jeremy
Author_Institution
Dept. of Ind. Eng. & Manage. Sci., Northwestern Univ., Evanston, IL, USA
fYear
2009
fDate
13-16 Dec. 2009
Firstpage
119
Lastpage
133
Abstract
Stochastic kriging is a methodology recently developed for metamodeling stochastic simulation. Stochastic kriging can partake of the behavior of kriging and of generalized least squares regression. This advanced tutorial explains regression, kriging, and stochastic kriging as metamodeling methodologies, emphasizing the consequences of misspecified models for global metamodeling. It provides an exposition of how to choose parameters in stochastic kriging and how to build a metamodel with it given simulation output, and discusses future research directions to enhance stochastic kriging.
Keywords
modelling; statistical analysis; stochastic processes; least squares regression; simulation metamodeling; stochastic kriging; Analytical models; Computational modeling; Industrial engineering; Metamodeling; Operations research; Predictive models; Response surface methodology; Steady-state; Stochastic processes; Stochastic systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Simulation Conference (WSC), Proceedings of the 2009 Winter
Conference_Location
Austin, TX
Print_ISBN
978-1-4244-5770-0
Type
conf
DOI
10.1109/WSC.2009.5429320
Filename
5429320
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