Biased control-variate estimation

IIE Transactions (Institute of Industrial Engineers)

Volumn 33, Issue 3, 2001, Pages 219-228

  Schmeiser, Bruce W ^a   Taaffe, Michael R ^b   Wang, Jin ^c  

^a PURDUE UNIVERSITY   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c Vahlosta State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ERROR ANALYSIS; OPTIMAL CONTROL SYSTEMS; PARAMETER ESTIMATION; STOCHASTIC CONTROL SYSTEMS; VARIATIONAL TECHNIQUES;

BIASED CONTROL VARIATES (BCV); CLASSICAL ESTIMATOR;

COMPUTER SIMULATION;

EID: 0035277750     PISSN: 0740817X     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007603516261     Document Type: Article

References (18)

1. Glynn, P.W. and Iglehart, D.L. (1989) The optimal linear combination of control variâtes in the presence of asymptotically negligible bias. Naval Research Logistics, 36, 683-692.
2. Hammersley, J.M. and Handscomb, D.C. (1964) Monte Carlo Methods, Methuen and Co. Ltd., London.
3. Lavenberg, S.S. and Welch, P.D. (1981) A perspective on the use of control variables to increase the efficiency of Monte Carlo simulations. Management Science, 27, 322-335.
4. Law, A.M. and Kelton, W.D. (2000) Simulation Modeling and Analysis, 2nd edn., McGraw-Hill, New York.
5. Lehmann, E.L. (1983) Point Estimation, John Wiley and Sons, New York.
6. Nelson, B.L. (1987) On control variate estimators. Computers ami Operations Research, 14, 219-225.
7. Nelson, B.L. (1989) Batch size effects on the efficiency of control variâtes in simulation. European Journal of Operational Research, 43, 184-196.
8. Nelson, B.L. (1990) Control variate remedies. Operations Research, 38, 974-992.
9. Nelson, B.L. and Schmeiser, B.W.,(1986) Decomposition of some wellknown variance reduction techniques. Journal of Statistical Computation and Simulation, 4, 27-46.
10. Nelson, B.L., Schmeiser, B.W., Taaffe, M.R. and Wang, J. (1997) Approximation-assisted point estimation. Operations Research Letters, 20, 109-118.
11. Schmeiser, B.W. (1990) Simulation experiments, in Handbooks in Operations Research and Afanagement Science, Volume 2: Stochastic Models, Heyman, O.P. and Sobel, M.J. (eds), North-Holland, Amsterdam, pp. 295-330.
12. Schmeiser, B.W. and Taaffe, M.R. (1994) Time-dependent queueing network approximations as simulation external control variâtes. Operations Research Letters, 16, 1-9.
13. Sharon, A.P. and Nelson, B.L. (1988) Analytic and external control variâtes for queueing network simulation. Journal of the Operational Research Society, 39, 595-602.
14. Swain, J.J. and 'Schmeiser, B.W. (1989) Control variâtes for Monte Carlo analysis of nonlinear statistical models, I: overview. Communications in Statistics: B - Simulation and Computation, 18(3), 1011-1036.
15. Taaffe, M.R. (1982) Approximating Nonstationary Queueing Models. Ph.D. Dissertation, Department of Industrial and Systems Engineering, The Ohio State University.
16. Taaffe, M.R. and Horn, S.A. (1983) External control variance reduction for nonstationary simulation, in Proceedings of the Winter Simulation Conference, IEEE, Piscataway, NJ, pp. 341-343.
17. Wang, J. (1994) Contributions to Monte Carlo Analysis: Variance Reduction, Random Search, and Bayesian Analysis. Ph.D. Dissertation, School of Industrial Engineering, Purdue University.
18. Wilson, J. (1984) Variance reduction techniques for digital simulation. American Journal of Mathematical and Management Sciences, 4, 277-312. Brace W. Schmeiser is a Professor in the School of Industrial Engineering at Purdue University. He received his Ph.D. from the School of Industrial and Systems Engineering at Georgia Tech in 1975. His primary research interests are the probabilistic and statistical aspects of digital-computer simulation. He is an active participant in the Winter Simulation Conference, including being Program Chair in 1983 and chairing the Board of Directors during 1988-1990. He is also active in the American Statistical Association, HE, INFORMS, and Omega Rho, the Operations Research Honor Society. Michael R. TaafTe is an Associate Professor of Operations and Management Science, Industrial Engineering, and Scientific Computation at the University of Minnesota. He received his Ph.D. from the Department of Industrial and Systems Engineering at The Ohio State University in 1982. His primary research interests are computational aspects of applied probability modeling and analysis, Monte Carlo simulation methodology, and combined use of simulation methods and computational probability methods. He is an active participant in both the simulation and applied probability research communities and has served as President of the INFORMS College on Simulation (1998-2000), and Director of the Operations Research Division of IIE (1997-1998). Jin Wang is an Associate Professor of Mathematics and Computer Science at Valdosta State University. He received his Ph.D. from the School of Industrial Engineering at Purdue University in 1994. His primary research interests are stochastic simulation, stochastic modeling and optimization, applied probability and statistics, and queueing methods for services and manufacturing. He is a member of INFORMS, INFORMS College on Simulation, HE, Omega Rho, and Pi Mu Epsilon.