Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling
Published Online:26 Oct 2020https://doi.org/10.1287/opre.2019.1978
References
- (1996) Maximum likelihood estimation for single server queues from waiting time data. Queueing Systems 24(1–4):155–167.Crossref, Google Scholar
- (2008) Parameter estimation using partial information with applications to queueing and related models. Statist. Probab. Lett. 78(12):1375–1383.Crossref, Google Scholar
- (2012) Inverse optimization: A new perspective on the Black-Litterman model. Oper. Res. 60(6):1389–1403.Link, Google Scholar
- (2017) Inverse optimization for the recovery of market structure from market outcomes: An application to the miso electricity market. Oper. Res. 65(4):837–855.Link, Google Scholar
- (2005) Inference in Hidden Markov Models (Springer, New York).Crossref, Google Scholar
- (2004) Feynman-Kac Formulae (Springer, New York).Crossref, Google Scholar
- (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. B. 39(1):1–38.Google Scholar
- (2001) Sequential Monte Carlo Methods (Wiley Online Library, Hoboken, NJ).Crossref, Google Scholar
- Dymarski P, ed., (2011) Hidden Markov Models, Theory and Applications (CRC Press, Boca Raton, FL).Crossref, Google Scholar
- (2018) Data-driven inverse optimization with imperfect information. Math. Programming 167(1):191–234.Crossref, Google Scholar
- (2004) Filtering recursions for calculating likelihoods for queues based on inter-departure time data. Statist. Comput. 14(3):261–266.Crossref, Google Scholar
- (2006) Gradient estimation. Henderson SG, Nelson BL, eds. Handbooks in Operations Research and Management Science, vol. 13 (Elsevier, Amsterdam), 575–616.Google Scholar
- (2015) Stochastic gradient estimation. Michael C, ed. Handbooks of Simulation Optimization (Springer, New York), 105–147.Crossref, Google Scholar
- (2009) Conditional Monte Carlo estimation of quantile sensitivities. Management Sci. 55(12):2019–2027.Link, Google Scholar
- (1991) Gradient Estimation via Perturbation Analysis (Kluwer Academic Publishers, Boston).Google Scholar
- (2020a) Computing sensitivity of distorted risk measure. INFORMS J. Comput. Forthcoming.Google Scholar
- (2020b) Technical note—Central limit theorems for estimated functions at estimated points. Oper. Res., ePub ahead of print May 21, https://doi.org/10.1287/opre.2019.1922.Google Scholar
- (2019) Optimization-based calibration of simulation input models. Oper. Res. 67(5):1362–1382.Link, Google Scholar
- (2016) A measure-valued differentiation approach to sensitivity analysis of quantiles. Math. Oper. Res. 41(1):293–317.Link, Google Scholar
- (2009) Estimating quantile sensitivities. Oper. Res. 57(1):118–130.Link, Google Scholar
- (2009) Simulating sensitivities of conditional value at risk. Management Sci. 55(2):281–293.Link, Google Scholar
- (2010) Pathwise estimation of probability sensitivities through terminating or steady-state simulations. Oper. Res. 58(2):357–370.Link, Google Scholar
- (2015) Technical note—On estimating quantile sensitivities via infinitesimal perturbation analysis. Oper. Res. 63(2):435–441.Link, Google Scholar
- (2001) Bayesian calibration of computer models. J. Royal Statist. Soc. Ser. B. Statist. Methodology 63(3):425–464.Crossref, Google Scholar
- (2003) Stochastic Approximation and Recursive Algorithms and Applications (Springer, New York).Google Scholar
- (1990) The queue inference engine: Deducing queue statistics from transactional data. Management Sci. 36(5):586–601.Link, Google Scholar
- (2018) Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation. J. Discrete Event Dynamic Systems 28(1):109–125.Crossref, Google Scholar
- (2009) Kernel estimation of quantile sensitivities. Naval Res. Logist. 56(6):511–525.Crossref, Google Scholar
- (1998) Sequential Monte Carlo methods for dynamic systems. J. Amer. Statist. Assoc. 93(443):1032–1044.Crossref, Google Scholar
- (2014) Gradient-based simulated maximum likelihood estimation for Lévy-driven Ornstein-Uhlenbeck stochastic volatility models. Quant. Finance 14(8):1399–1414.Crossref, Google Scholar
- (2016) Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions. Quant. Finance 16(9):1393–1411.Crossref, Google Scholar
- (2017) On the asymptotic analysis of quantile sensitivity estimation by Monte Carlo simulation. Chan WKV, D’Ambrogio A, Zacharewicz G, Mustafee N, Wainer G, Page E, eds. Proc. 2017 Winter Simulation Conf. (IEEE, Piscataway, NJ), 2336–2347.Google Scholar
- (2018) A new unbiased stochastic derivative estimator for discontinuous sample performances with structural parameters. Oper. Res. 66(2):487–499.Link, Google Scholar
- (1997) Estimation for an M/G/∞ queue with incomplete information. Biometrika 84(2):295–308.Crossref, Google Scholar
- (2015) Unbiased estimation with square root convergence for sde models. Oper. Res. 63(5):1026–1043.Google Scholar
- (2007) Estimation for queues from queue length data. Queueing Systems 55(2):131–138.Crossref, Google Scholar
- (1988) Real Analysis, 3rd ed. (Macmillan, New York).Google Scholar
- (1964) Principles of Mathematical Analysis (McGraw-Hill Education, New York).Google Scholar
- (2003) Mathematical Statistics (Springer, New York).Crossref, Google Scholar
- (1988) Perturbation analysis gives strongly consistent sensitivity estimates for the M/G/1 queue. Management Sci. 34(1):39–64.Link, Google Scholar
- (2005) Inverse Problem Theory and Methods for Model Parameter Estimation (SIAM, Philadelphia).Crossref, Google Scholar
- (2000) Asymptotic Statistics (Cambridge University Press, Cambridge, UK).Google Scholar
- (2006) Maximum likelihood estimates and confidence intervals of an M/M/R queue with heterogeneous servers. Math. Methods Oper. Res. 63(2):371–384.Crossref, Google Scholar
- (1982) Approximating a point process by a renewal process, I: Two basic methods. Oper. Res. 30(1):125–147.Link, Google Scholar

