Likelihood ratio gradient estimation for stochastic systems
Communications of the ACM - Special issue on simulation
The asymptotic efficiency of simulation estimators
Operations Research
Simulating Sensitivities of Conditional Value at Risk
Management Science
Estimating Quantile Sensitivities
Operations Research
Gradient estimation for discrete-event systems by measure-valued differentiation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Weak Differentiability of Product Measures
Mathematics of Operations Research
A Perturbation Analysis Approach to Phantom Estimators for Waiting Times in the G/G/1 Queue
Discrete Event Dynamic Systems
On the controversy over tailweight of distributions
Operations Research Letters
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Quantiles are important performance characteristics that have been adopted in many areas for measuring the quality of service. Recently, sensitivity analysis of quantiles has attracted quite some attention. Sensitivity analysis of quantiles is particularly challenging as quantiles cannot be expressed as the expected value of some sample performance function, and it is therefore not evident how standard gradient estimation methods can be applied. In this paper we present a straightforward quantile sensitivity estimator based on measure-valued differentiation (MVD). We compare our new estimator with the known infinitesimal perturbation analysis (IPA) estimator and discuss implementation issues. Numerical examples will illustrate our results.