Simulation Modeling and Analysis
Simulation Modeling and Analysis
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Control variates can be applied to Monte Carlo sampling experiments to improve the precision of the results. This method is especially useful in statistical problems were low order approximators of a particular variate of interest are available and possibly several statistical properties of the variate are to be investigated. In this paper a control variate scheme based on the linear approximator &sgr; of the nonlinear parameter estimator (@@@@) is used to improve the precision of the first four moments of (@@@@) and the covariance matrix of the paramter estimates. The control variate method is shown to improve the effectiveness of the Monte Carlo results without substantially increasing the estimation effort, and it is effective over a wide range of nonlinearities. An approximate expression for the effectiveness of the control variate method based on the Beale measure of nonlinearity N&thgr; is given.