Pathwise estimation of probability sensitivities through terminating or steady-state simulations

Pathwise estimation of probability sensitivities through terminating or steady-state simulations

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Article ID: iaor20104019
Volume: 58
Issue: 2
Start Page Number: 357
End Page Number: 370
Publication Date: Mar 2010
Journal: Operations Research
Authors: ,
Keywords: simulation: analysis
Abstract:

A probability is the expectation of an indicator function. However, the standard pathwise sensitivity estimation approach, which interchanges the differentiation and expectation, cannot be directly applied because the indicator function is discontinuous. In this paper, we design a pathwise sensitivity estimator for probability functions based on a result of Hong (2009). We show that the estimator is consistent and follows a central limit theorem for simulation outputs from both terminating and steady-state simulations, and the optimal rate of convergence of the estimator is n -2/5 where n is the sample size. We further demonstrate how to use importance sampling to accelerate the rate of convergence of the estimator to n -1/2, which is the typical rate of convergence for statistical estimation. We illustrate the performances of our estimators and compare them to other well-known estimators through several examples.

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