Control Variates for Monte Carlo Analysis of Nonlinear Statistical Models,I: Overview |
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Authors: | James J. Swain Bruce W. Schmeiser |
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Affiliation: | 1. School of Industrial and Systems Engineering , Georgia Institute of Technology , 43332, Atlanta, GA;2. School of Industrial Engineering , Purdue University , 47907, West Lafayette, IN |
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Abstract: | Parameter values of nonlinear statistical models are typically estimated from data using iterative numerical procedures. The resulting joint sampling distribution of the parameter estimators is often intractable, resulting in the use of approximators or Monte Carlo simulation to determine properties of the sampling distribution. This paper develops methods, using linear and higher-order approximators as control variates that reduce the variance of the Monte Carlo estimator by orders of magnitude. Estimation of means, higher-order raw moments, variances, covariances, and percentiles is considered. |
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Keywords: | control variates Monte Carlo nonlinear estimation simulation swindle variance reduction |
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