EconPapers    
Economics at your fingertips  
 

Control Variates for Probability and Quantile Estimation

Timothy C. Hesterberg and Barry L. Nelson
Additional contact information
Timothy C. Hesterberg: MathSoft, 1700 Westlake Avenue N., Suite 500, Seattle, Washington 98109
Barry L. Nelson: Department of Industrial Engineering and Management Science, 2145 Sheridan Road, Northwestern University, Evanston, Illinois 62208-3119

Management Science, 1998, vol. 44, issue 9, 1295-1312

Abstract: In stochastic systems, quantiles indicate the level of system performance that can be delivered with a specified probability, while probabilities indicate the likelihood that a specified level of system performance can be achieved. We present new estimators for use in simulation experiments designed to estimate such quantiles or probabilities of system performance. All of the estimators exploit control variates to increase their precision, which is especially important when extreme quantiles (in the tails of the distribution of system performance) or extreme probabilities (near zero or one) are of interest. Control variates are auxiliary random variables with known properties---in this case, known quantiles---and a strong stochastic association with the performance measure of interest. Since transforming a control variate can increase its effectiveness, we propose both continuous and discrete approximations to the optimal (variance-minimizing) transformation for estimating probabilities, and then invert the probability estimators to obtain corresponding quantile estimators. We also propose a direct control-variate quantile estimator that is not based on inverting a probability estimator. An empirical study using queueing, inventory and project-planning examples shows that substantial reductions in mean squared error can be obtained when estimating the 0.9, 0.95, and 0.99 quantiles.

Keywords: Simulation; Variance Reduction; Control Variates; Statistics (search for similar items in EconPapers)
Date: 1998
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.44.9.1295 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:44:y:1998:i:9:p:1295-1312

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().

 
Page updated 2025-03-19
Handle: RePEc:inm:ormnsc:v:44:y:1998:i:9:p:1295-1312