Extreme quantile estimation with nonparametric adaptive importance sampling

Quantile Estimation with Adaptive Importance Sampling

Nonparametric Estimation of Extreme Conditional Quantiles

The estimation of extreme conditional quantiles is an important issue in different scientific disciplines. Up to now, the extreme value literature focused mainly on estimation procedures based on i.i.d. samples. On the other hand, quantile regression based procedures work well for estimation within the data range i.e. the estimation of nonextreme quantiles but break down when main interest is i...

Nonparametric Adaptive Importance Sampling for Rare Event Simulation

Simulating rare events in telecommunication networks such as estimation for cell loss probability in Asynchronous Transfer Mode (ATM) networks requires a major simulation effort due to the slight chance of buffer overflow. Importance Sampling (IS) is applied to accelerate the occurrence of rare events. Importance Sampling depends on a biasing scheme to make the estimator from IS unbiased. Adapt...

Importance Sampling for Monte Carlo Estimation of Quantiles

This paper is concerned with applying importance sampling as a variance reduction tool for computing extreme quantiles. A central limit theorem is derived for each of four proposed importance sampling quantile estimators. EEciency comparisons are provided in a certain asymptotic setting, using ideas from large deviation theory.

Multi-element stochastic spectral projection for high quantile estimation

We investigate quantile estimation by multi–element generalized Polynomial Chaos (gPC) metamodel where the exact numerical model is approximated by complementary metamodels in overlapping domains that mimic the model’s exact response. The gPC metamodel is constructed by the non–intrusive stochastic spectral projection approach and function evaluation on the gPC metamodel can be considered as es...