5 papers
Conditional Leibniz Derivative Estimation with an Application to American Call Min-Options
Xingyu Ren, Michael C. Fu, Pierre L'Ecuyer
Leibniz derivative estimation is a Monte Carlo technique for estimating derivatives of a discontinuous sample performance in stochastic models with respect to parameters of interes…
Sensitivity analysis for stopping criteria with application to organ transplantations
Xingyu Ren, Michael C. Fu, Steven I. Marcus
We consider a stopping problem and its application to the decision-making process regarding the optimal timing of organ transplantation for individual patients. At each decision pe…
On Structural Properties of Risk-Averse Optimal Stopping Problems
Xingyu Ren, Michael C. Fu, Steven I. Marcus
We establish structural properties of optimal stopping problems under time-consistent dynamic (coherent) risk measures, focusing on value function monotonicity and the existence of…
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…
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,…