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Metamodel estimation using integrated correlation methods

This paper develops a generalized approach for combining the use of the Schruben-Margolin correlation induction strategy and control variates in a simulation experiment designed to estimate a metamodel that is linear in the unknown parameters relating the response variable of interest to selected exogenous decision variables. This generalized approach is based on standard techniques of regression analysis. Under certain broad assumptions, the combined use of the Schruben-Margolin correlation induction strategy and control variates is shown to give a more efficient estimator of the metamodel coefficients than each of the following conventional correlation-based variance reduction techniques: independent streams, common random numbers, control variates, and the Schruben-Margolin strategy.