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BETTER SIMULATION METAMODELING: THE WHY, WHAT, AND HOW OF STOCHASTIC KRIGING

By M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, R. G. Ingalls and Jeremy Staum

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. 1 INTRODUCTION: SIMULATIO

Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.416.2107
Provided by: CiteSeerX
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