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On the variance of single-run unbiased stochastic derivative estimators

Cui, Zhenyu; Fu, Michael; Hu, Jian-Qiang; Liu, Yanchu; … - In: INFORMS journal on computing : JOC 32 (2020) 2, pp. 390-407

Persistent link: https://www.econbiz.de/10012242769

A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters

Peng, Yijie; Fu, Michael; Hu, Jian-Qiang; Heidergott, Bernd - In: Operations research 66 (2018) 2, pp. 487-499

Persistent link: https://www.econbiz.de/10011845997

Maximum likelihood estimation by Monte Carlo simulation : toward data-driven stochastic modeling

Peng, Yijie; Fu, Michael; Heidergott, Bernd; Lam, Henry - In: Operations research 68 (2020) 6, pp. 1896-1912

Persistent link: https://www.econbiz.de/10012392175

On estimating quantile sensitivities via infinitesimal perturbation analysis

Jiang, Guangxin; Fu, Michael - In: Operations research 63 (2015) 2, pp. 435-441

Persistent link: https://www.econbiz.de/10010526698

Sequential selection with unknown correlation structures

Qu, Huashuai; Ryzhov, Ilya O.; Fu, Michael; Ding, Zi - In: Operations research 63 (2015) 4, pp. 931-948

Persistent link: https://www.econbiz.de/10011313170

Efficient simulation budget allocation for selecting an optimal subset

Chen, Chun-hung; He, Donghai; Fu, Michael; Lee, Loo Hay - In: INFORMS journal on computing : JOC 20 (2008) 4, pp. 579-595

Persistent link: https://www.econbiz.de/10003772390

Regression models augmented with direct stochastic gradient estimators

Fu, Michael; Qu, Huashuai - In: INFORMS journal on computing : JOC 26 (2014) 3, pp. 484-499

Persistent link: https://www.econbiz.de/10010399808

Technical note: central limit theorems for estimated functions at estimated points

Glynn, Peter W.; Fan, Lin; Fu, Michael; Hu, Jian-Qiang; … - In: Operations research 68 (2020) 5, pp. 1557-1563

Persistent link: https://www.econbiz.de/10012301573

Optimal unbiased estimation for expected cumulative discounted cost

Cui, Zhenyu; Fu, Michael; Peng, Yijie; Zhu, Lingjiong - In: European journal of operational research : EJOR 286 (2020) 2, pp. 604-618

Persistent link: https://www.econbiz.de/10012291551

Variance reduction for generalized likelihood ratio method by conditional Monte Carlo and randomized Quasi-Monte Carlo methods

Peng, Yijie; Fu, Michael; Hu, Jiaqiao; L'Ecuyer, Pierre; … - In: Journal of management science and engineering 7 (2022) 4, pp. 550-577

The generalized likelihood ratio (GLR) method is a recently introduced gradient estimation method for handling discontinuities in a wide range of sample performances. We put the GLR methods from previous work into a single framework, simplify regularity conditions to justify the unbiasedness of...

Persistent link: https://www.econbiz.de/10014315671