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
Link To Document :
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