Rare-event Probability Estimation via Empirical Likelihood Maximization
Computation
2013-12-12 v1
Abstract
We explore past and recent developments in rare-event probability estimation with a particular focus on a novel Monte Carlo technique Empirical Likelihood Maximization (ELM). This is a versatile method that involves sampling from a sequence of densities using MCMC and maximizing an empirical likelihood. The quantity of interest, the probability of a given rare-event, is estimated by solving a convex optimization program related to likelihood maximization. Numerical experiments are performed using this new technique and benchmarks are given against existing robust algorithms and estimators.
Cite
@article{arxiv.1312.3027,
title = {Rare-event Probability Estimation via Empirical Likelihood Maximization},
author = {A. Huang and Z. I. Botev},
journal= {arXiv preprint arXiv:1312.3027},
year = {2013}
}