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26 11 im noticia probabilitywebinarTítulo: Importance sampling with adaptive winsorization

Palestrante: Paulo Orenstein (IMPA)

Data: 30/11/2020
Local: Transmissão online

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Resumo: Importance sampling is a widely used technique to estimate the properties of a distribution. The resulting estimator is always unbiased, but may sometimes incur huge or infinite variance. This work investigates trading-off some bias for variance by winsorizing the importance sampling estimator using an adaptive thresholding procedure based on the Balancing Principle (also known as Lepskii's Method). This provides a principled way to perform winsorization, with finite-sample optimality guarantees and good empirical performance.