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stat.ME2025
Stochastic Derivative Estimation for Discontinuous Sample Performances: A Leibniz Integration Perspective
Xingyu Ren, Michael C. Fu, Pierre L'Ecuyer
We develop a novel stochastic derivative estimation framework for sample performance functions that are discontinuous in the parameter of interest, based on the multidimensional Le…
stat.ME2025
New Bounds and Truncation Boundaries for Importance Sampling
Yijuan Liang, Guangxin Jiang, Michael C. Fu
Importance sampling (IS) is a technique that enables statistical estimation of output performance at multiple input distributions from a single nominal input distribution. IS is co…
stat.ME2025
Generalizing the Generalized Likelihood Ratio Method Through a Push-Out Leibniz Integration Approach
Xingyu Ren, Michael C. Fu
We generalize the generalized likelihood ratio (GLR) method through a novel push-out Leibniz integration approach. Extending the conventional push-out likelihood ratio (LR) method,…