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• W2279857902 abstract "Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captured by the approximating distribution, in which case more stable estimates can be obtained by modifying extreme importance ratios. We present a new method for stabilizing importance weights using a generalized Pareto distribution fit to the upper tail of the distribution of the simulated importance ratios. The method, which empirically performs better than existing methods for stabilizing importance sampling estimates, includes stabilized effective sample size estimates, Monte Carlo error estimates, and convergence diagnostics. The presented Pareto $hat{k}$ finite sample convergence rate diagnostic is useful for any Monte Carlo estimator." @default.
• W2279857902 created "2016-06-24" @default.
• W2279857902 creator A5001332022 @default.
• W2279857902 creator A5047269552 @default.
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• W2279857902 date "2015-07-09" @default.
• W2279857902 modified "2023-09-26" @default.
• W2279857902 title "Pareto Smoothed Importance Sampling" @default.
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• W2279857902 doi "https://doi.org/10.48550/arxiv.1507.02646" @default.
• W2279857902 hasPublicationYear "2015" @default.
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