A recursive conditioning approach is added to Leibniz derivative estimation to produce a low-variance, LR-free estimator for derivatives in stochastic models with discontinuities, tested on American call min-options.
arXiv preprint arXiv:2511.00006 , year=
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New recursive estimators for higher-order derivatives of mean queueing time in single-server queues are derived from the Lindley equation via the Leibniz integral rule using single sample paths.
citing papers explorer
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Conditional Leibniz Derivative Estimation with an Application to American Call Min-Options
A recursive conditioning approach is added to Leibniz derivative estimation to produce a low-variance, LR-free estimator for derivatives in stochastic models with discontinuities, tested on American call min-options.
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Augmenting Automatic Differentiation for a Single-Server Queue via the Leibniz Integral Rule
New recursive estimators for higher-order derivatives of mean queueing time in single-server queues are derived from the Lindley equation via the Leibniz integral rule using single sample paths.