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Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation
We provide applications of the generalized likelihood ratio (GLR) method proposed in Peng
et al.(2016c) to distribution sensitivity estimation for both finite-horizon and steady-state …
et al.(2016c) to distribution sensitivity estimation for both finite-horizon and steady-state …
On the asymptotic analysis of quantile sensitivity estimation by Monte Carlo simulation
We provide a unified framework to treat the asymptotic analysis for the non-batched quantile
sensitivity estimators of Fu et al.(2009), Liu and Hong (2009), and Lei et al.(2017). With only …
sensitivity estimators of Fu et al.(2009), Liu and Hong (2009), and Lei et al.(2017). With only …
Estimating quantile sensitivity for financial models with correlations and jumps
We apply a generalized likelihood ratio (GLR) derivative estimation method in previous
works to estimate quantile sensitivity of financial models with correlations and jumps …
works to estimate quantile sensitivity of financial models with correlations and jumps …
Sensitivity analysis of ranked data: from order statistics to quantiles
W Volk-Makarewicz, B Heidergott - Discrete Event Dynamic Systems, 2015 - Springer
In this paper we provide the mathematical theory for sensitivity analysis of order statistics of
continuous random variables, where the sensitivity is with respect to a distributional …
continuous random variables, where the sensitivity is with respect to a distributional …
[PDF][PDF] ON THE ASYMPTOTIC ANALYSIS OF QUANTILE SENSITIVITY ESTIMATION BY MONTE CARLO SIMULATION
We provide a unified framework to treat the asymptotic analysis for the non-batched quantile
sensitivity estimators of Fu et al.(2009), Liu and Hong (2009), and Lei et al.(2017). With only …
sensitivity estimators of Fu et al.(2009), Liu and Hong (2009), and Lei et al.(2017). With only …