Variance Reduction of Quantile Estimates via Nonlinear Controls
Lewis, Peter A.W.
Ressler, Richard L.
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Linear controls are a well known techniques for achieving variance reduction in computer simulation. Unfortunately the effectiveness of a linear control depends upon the correlation between the statistic of interest and control which is often low. Since statistics are often nonlinear functions of the control this implies that nonlinear controls offer a means for improvement over linear controls. Nonlinear controls have had success in increasing the variance reduction over a linear control. This current work focuses on the use of nonlinear controls for reducing the variance of quantile estimates. The paper begins with a short discussion of linear controls. It describes nonlinear controls and the possibility for improved performance. The final sections discuss quantiles as controls and potential nonlinear controls for variance reduction in quantile estimation.
Proceedings of the 1989 Winter Simulation Conference, E.A. MacNair, K.J. Musselman, P. Heidelberger (eds.)
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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Lewis, Peter A. W.; Ressler, Richard L. (Monterey, California. Naval Postgraduate School, 1990-04); NPS-55-90-09Linear controls are a well known simple technique for achieving variance reduction in computer simulation. Unfortunately the effectiveness of a linear control depends upon the correlation between the statistic of interest ...
An investigation of nonlinear controls and regression-adjusted estimators for variance reduction in computer simulation. Ressler, Richard L. (Monterey, California. Naval Postgraduate School, 1991-03);This dissertation develops new techniques for variance reduction in computer simulation. It demonstrates that applying nonlinear transformations to control variables can increase their effectiveness over linear controls. ...
Lewis, Peter A.W.; Ressler, Richard L.; Wood, R. Kevin (Monterey, California: Naval Postgraduate School., 1988-08); NPS55-88-007Nonlinear regression-adjusted control variables are investigated for improving variance reduction in statistical and systems simulations. To this end, simple control variables are piecewise sectioned and then transformed ...