Name
Abstract | ||
|---|---|---|
AbstractWe analyze the variance of single-run unbiased stochastic derivative estimators. The distribution of a specific conditional expectation characterizes an intrinsic distributional property of the derivative estimators in a given class, which, in turn, separates two of the most popular single-run unbiased derivative estimators, infinitesimal perturbation analysis and the likelihood ratio method, into disjoint classes. In addition, a necessary and sufficient condition for the estimators to achieve the lowest variance in a certain class is provided, as well as insights into finding an estimator with lower variance. We offer a sufficient condition to substantiate the rule of thumb that the infinitesimal perturbation analysis estimator has a smaller variance than does the likelihood ratio method estimator and to provide a counterexample when the sufficient condition is not satisfied. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1287/ijoc.2019.0897 | Periodicals |
Keywords | DocType | Volume |
simulation, stochastic derivative estimation, variance, infinitesimal perturbation analysis, likelihood ratio method | Journal | 32 |
Issue | ISSN | Citations |
2 | 1526-5528 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Cui | 1 | 8 | 3.94 |
| Michael C. Fu | 2 | 1161 | 128.16 |
| Jian-Qiang Hu | 3 | 304 | 39.79 |
| Yanchu Liu | 4 | 7 | 3.56 |
| Yijie Peng | 5 | 32 | 12.59 |
| Lingjiong Zhu | 6 | 19 | 7.41 |