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Using Simulation to Estimate First Passage Distribution
Ross, Sheldon M.; Schechner, Zvi - In: Management Science 31 (1985) 2, pp. 224-234
the problem of using simulation to estimate the number of transitions it takes the process to enter \scr{A}. Using …
Persistent link: https://www.econbiz.de/10009209093
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Using Common Random Numbers for Indifference-Zone Selection and Multiple Comparisons in Simulation
Nelson, Barry L.; Matejcik, Frank J. - In: Management Science 41 (1995) 12, pp. 1935-1945
We present a general recipe for constructing experiment design and analysis procedures that simultaneously provide indifference-zone selection and multiple-comparison inference for choosing the best among k simulated systems. We then exhibit two such procedures that exploit the...
Persistent link: https://www.econbiz.de/10009209251
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Variance Reduction via Lattice Rules
L'Ecuyer, Pierre; Lemieux, Christiane - In: Management Science 46 (2000) 9, pp. 1214-1235
how they can be used effectively for the simulation models that are typically encountered in the area of management …
Persistent link: https://www.econbiz.de/10009214185
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Probabilistic Error Bounds for Simulation Quantile Estimators
Jin, Xing; Fu, Michael C.; Xiong, Xiaoping - In: Management Science 49 (2003) 2, pp. 230-246
a simulation-based quantile estimator fails to lie in a prespecified neighborhood of the true quantile. First, we show …
Persistent link: https://www.econbiz.de/10009218121
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Variance reduction by antithetic random numbers of Monte Carlo methods for unrestricted and reflecting diffusions
Costantini, C. - In: Mathematics and Computers in Simulation (MATCOM) 51 (1999) 1, pp. 1-17
The main discretization schemes for diffusion processes, both unrestricted and reflecting in a hyper-rectangle, are considered. For every discretized path, an `antithetic' path is obtained by changing the sign of the driving random variables, which are chosen symmetric. It is shown that, under...
Persistent link: https://www.econbiz.de/10010750125
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Control-Variate Models of Common Random Numbers for Multiple Comparisons with the Best
Nelson, Barry L.; Hsu, Jason C. - In: Management Science 39 (1993) 8, pp. 989-1001
Using common random numbers (CRN) in simulation experiment design is known to reduce the variance of estimators of …, specifically multiple comparisons with the best. These models explain the effect of CRN via a linear regression of the simulation … output on "control variates" that are functions of the simulation inputs. We establish theoretically, and illustrate …
Persistent link: https://www.econbiz.de/10009203702
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50th Anniversary Article: Stochastic Simulation Research in Management Science
Nelson, Barry L. - In: Management Science 50 (2004) 7, pp. 855-868
When the simulation department of Management Science was created in 1978 it ushered in an era of significant … methodological advances in stochastic simulation. However, the foundation for the field---not just the work that has been published … in Management Science---was provided by two papers published long before simulation had its own department in the journal …
Persistent link: https://www.econbiz.de/10009204174
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A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations
Lavenberg, S. S.; Welch, P. D. - In: Management Science 27 (1981) 3, pp. 322-335
. The emphasis is on the practical problems and potential of applying the method in the simulation of complex systems. The …
Persistent link: https://www.econbiz.de/10009191110
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Experimental Evaluation of Variance Reduction Techniques for Queueing Simulation Using Generalized Concomitant Variables
Wilson, J. R.; Pritsker, A. A. B. - In: Management Science 30 (1984) 12, pp. 1459-1472
concomitant variables that are defined on each input process sampled during a queueing simulation. In this paper we present an …
Persistent link: https://www.econbiz.de/10009191264
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Control Variates for Quantile Estimation
Hsu, Jason C.; Nelson, Barry L. - In: Management Science 36 (1990) 7, pp. 835-851
New point and interval estimators for quantiles that employ a control variate are introduced. The properties of these estimators do not depend on the usual assumption of joint normality between the random variable of interest and the control. Illustrative examples for queueing and stochastic...
Persistent link: https://www.econbiz.de/10009191658