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We provide applications of the generalized likelihood ratio (GLR) method proposed in Peng et al. (2016c) to distribution sensitivity estimation for both finite-horizon and steady-state simulation. Applications on sensitivity of distortion risk measure, gradient-based maximum likelihood estimation, and quantile sensitivity in both finite-horizon and steady-state settings are put together under a single...
We propose a generalized likelihood ratio estimator for the distribution sensitivity in a general framework. Applications on quantile sensitivity, sensitivity of distortion risk measure, and gradient-based maximum likelihood estimation are put together under a single umbrella, and addressed uniformly by the proposed estimator. Numerical experiments substantiate the efficiency of the new method.
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