Oracle Importance Sampling for Stochastic Simulation Models
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We consider the problem of estimating an expected outcome from a stochastic simulation model using importance sampling. We propose a two-stage procedure that involves a regression stage and a sampling stage to construct our estimator. We introduce a parametric and a nonparametric regression estimator in the first stage and study how the allocation between the two stages affects the performance of final estimator. We derive the oracle property for both approaches. We analyze the empirical performances of our approaches using two simulated data and a case study on wind turbine reliability evaluation.
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