Simulation metamodel estimation using a combined correlation-based variance reduction technique for first and higher-order metamodels

Abstract

This paper develops a procedure for jointly applying the correlation-based variance reduction techniques of common random numbers and control variates in a simulation experiment that is designed to estimate a linear metamodel (that is, linear in the regression model parameters) for a single response variable expressed in terms of an input vector of design variables for the target system. This procedure combines (a) the method of common random numbers for metamodel estimation and (b) a metamodel estimation scheme based on the method of control variates. Under specified conditions on the dependency structure of the simulation outputs and with respect to a variety of optimality criteria, the combined procedure is shown to be superior to each of the following conventional correlation-based variance reduction techniques: independent random number streams, common random number streams, and control variates.