Technical Note—Central Limit Theorems for Estimated Functions at Estimated Points
Published Online:21 May 2020https://doi.org/10.1287/opre.2019.1922
References
- (2007) Stochastic Simulation: Algorithms and Analysis (Springer, New York).Crossref, Google Scholar
- (1999) Convergence of Probability Measures (John Wiley & Sons, New York).Crossref, Google Scholar
- (1994) A note on the central limit theorem for stochastically continuous processes. Stochastic Processes Appl. 53(2):351–361.Crossref, Google Scholar
- (2009) Conditional Monte Carlo estimation of quantile sensitivities. Management Sci. 55(12):2019–2027.Link, Google Scholar
- (2014) Monte Carlo methods for value-at-risk and conditional value-at-risk: A review. ACM Trans. Model. Comput. Simulation 24(4):1–37.Crossref, Google Scholar
- (1997) A batch means methodology for estimation of a nonlinear function of a steady-state mean. Management Sci. 43(8):1121–1135.Link, Google Scholar
- (2017) On the asymptotic analysis of quantile sensitivity estimation by Monte Carlo simulation. Proc. 2017 Winter Simul. 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
- (2010) Asymptotic distribution of law-invariant risk functionals. Finance Stochastics 14(3):397–418.Crossref, Google Scholar
- (1980) Approximation Theorems of Mathematical Statistics (John Wiley & Sons, New York).Crossref, Google Scholar
- (2009) Lectures on Stochastic Programming: Modeling and Theory (Society for Industrial and Applied Mathematics, Philadelphia).Crossref, Google Scholar
- (1986) Density Estimation for Statistics and Data Analysis (Chapman and Hall/CRC, Boca Raton, FL).Crossref, Google Scholar

