ESTIMATING SIMULATION METAMODELS USING COMBINED CORRELATION-BASED VARIANCE REDUCTION TECHNIQUES

Abstract

This paper develops a procedure for jointly applying all of the correlation-based variance reduction techniques (namely, die methods of antithetic variates, common random numbers, and control variates) in a simulation experiment that is designed to estimate a linear metamodel (that is, a linear regression model) for a single response variable expressed in terms of an input vector of design variables for the target system. This procedure combines (a) the Schruben-Margolin strategy for metamodel estimation based on joint application of the methods of common random numbers and antithetic variates, and (b) a metamodel estimation scheme based on the method of control variates. Under specified conditions on the dependency structure of die simulation outputs and with respect to a variety of optimally 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, control variates, and the original Schruben-Margolin strategy.

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