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2019 Importance sampling and its optimality for stochastic simulation models
Yen-Chi Chen, Youngjun Choe
Electron. J. Statist. 13(2): 3386-3423 (2019). DOI: 10.1214/19-EJS1604

Abstract

We consider the problem of estimating an expected outcome from a stochastic simulation model. Our goal is to develop a theoretical framework on importance sampling for such estimation. By investigating the variance of an importance sampling estimator, we propose a two-stage procedure that involves a regression stage and a sampling stage to construct the final 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 the final estimator. We analyze the variance reduction rates and derive oracle properties of both methods. We evaluate the empirical performances of the methods using two numerical examples and a case study on wind turbine reliability evaluation.

Citation

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Yen-Chi Chen. Youngjun Choe. "Importance sampling and its optimality for stochastic simulation models." Electron. J. Statist. 13 (2) 3386 - 3423, 2019. https://doi.org/10.1214/19-EJS1604

Information

Received: 1 October 2018; Published: 2019
First available in Project Euclid: 25 September 2019

zbMATH: 07113721
MathSciNet: MR4010983
Digital Object Identifier: 10.1214/19-EJS1604

Subjects:
Primary: 62G20
Secondary: 62G86, 62H30

JOURNAL ARTICLE
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Vol.13 • No. 2 • 2019
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