most citedGeneralizing the Generalized Likelihood Ratio Method Through a Push-Out Leibniz Integration Approach

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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★ 1 cited

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,…