Robust Importance Sampling with Adaptive Winsorization
Computation
2021-02-10 v2 Data Analysis, Statistics and Probability
Methodology
Abstract
Importance sampling is a widely used technique to estimate properties of a distribution. This paper investigates trading-off some bias for variance by adaptively winsorizing the importance sampling estimator. The novel winsorizing procedure, based on the Balancing Principle (or Lepskii's Method), chooses a threshold level among a pre-defined set by roughly balancing the bias and variance of the estimator when winsorized at different levels. As a consequence, it provides a principled way to perform winsorization with finite-sample optimality guarantees under minimal assumptions. In various examples, the proposed estimator is shown to have smaller mean squared error and mean absolute deviation than leading alternatives.
Cite
@article{arxiv.1810.11130,
title = {Robust Importance Sampling with Adaptive Winsorization},
author = {Paulo Orenstein},
journal= {arXiv preprint arXiv:1810.11130},
year = {2021}
}