Name
Abstract | ||
|---|---|---|
We investigate three alternatives for combining a deterministic approximation with a stochastic simulation estimator: (1) binary choice, (2) linear combination, and (3) Bayesian analysis. Making a binary choice, based on compatibility of the simulation estimator with the approximation, provides at best a 20% improvement in simulation efficiency. More effective is taking a linear combination of the approximation and the simulation estimator using weights estimated from the simulation data, which provides at best a 50% improvement in simulation efficiency. The Bayesian analysis yields a linear combination with weights that are a function of the simulation data and the prior distribution on the approximation error; the efficiency depends upon the quality of the prior distribution. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1016/S0167-6377(96)00053-3 | Oper. Res. Lett. |
Keywords | Field | DocType |
linear combination,binary choice,stochastic simulation estimator,bayesian analysis,simulation,simulation efficiency,prior distribution,approximation-assisted point estimation,biased estimation,monte carlo,approximation error,simulation data,simulation estimator,deterministic approximation,control variates,point estimation,stochastic simulation | Stochastic simulation,Point estimation,Linear combination,Mathematical optimization,Monte Carlo method,Control variates,Prior probability,Mathematics,Approximation error,Estimator | Journal |
Volume | Issue | ISSN |
20 | 3 | Operations Research Letters |
Citations | PageRank | References |
8 | 3.16 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Barry L. Nelson | 1 | 1876 | 257.62 |
| Bruce W. Schmeiser | 2 | 564 | 134.32 |
| Michael R. Taaffe | 3 | 64 | 17.75 |
| Jin Wang | 4 | 10 | 4.73 |