DocumentCode :
3029143
Title :
Stochastic kriging with qualitative factors
Author :
Xi Chen ; Kai Wang ; Feng Yang
Author_Institution :
Stat. Sci. & Oper. Res., Virginia Commonwealth Univ., Richmond, VA, USA
fYear :
2013
fDate :
8-11 Dec. 2013
Firstpage :
790
Lastpage :
801
Abstract :
Stochastic kriging (SK) has been studied as an effective metamodeling technique for approximating the mean response surface implied by a stochastic simulation. Until recently, it has only been applied to simulation experiments with continuous decision variables or factors. In this paper, we propose a new method called stochastic kriging with qualitative factors (SKQ) that extends stochastic kriging to a broader scope of applicability. SKQ is able to build metamodels for stochastic simulations that have both quantitative (continuous) and qualitative (categorical) factors. To make this extension, we introduce basic steps of constructing valid spatial correlation functions for handling correlations across levels of qualitative factors. Two examples are used to demonstrate the advantages of SKQ in aggregating information from related response surfaces and metamodeling them simultaneously, in addition to maintaining SK´s ability of effectively tackling the impact of simulation errors.
Keywords :
response surface methodology; simulation; statistical analysis; stochastic processes; SKQ; mean response surface; metamodeling technique; qualitative factor; spatial correlation function; stochastic kriging; stochastic simulation; Computational modeling; Correlation; Covariance matrices; Response surface methodology; Stochastic processes; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Simulation Conference (WSC), 2013 Winter
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4799-2077-8
Type :
conf
DOI :
10.1109/WSC.2013.6721471
Filename :
6721471
Link To Document :
بازگشت