Deterministic computation of quantiles in a Lipschitz framework
From MaRDI portal
Publication:6664849
DOI10.1016/j.cam.2024.116344MaRDI QIDQ6664849
Publication date: 16 January 2025
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Analysis of algorithms and problem complexity (68Q25) Probabilistic models, generic numerical methods in probability and statistics (65C20) Approximations to statistical distributions (nonasymptotic) (62E17) Algorithms for approximation of functions (65D15) Approximation algorithms (68W25)
Cites Work
- Unnamed Item
- Unnamed Item
- Fluctuation analysis of adaptive multilevel splitting
- Simulation and estimation of extreme quantiles and extreme probabilities
- Sequential Monte Carlo for rare event estimation
- Controlled stratification for quantile estimation
- Smoothed jackknife empirical likelihood for the one-sample difference of quantiles
- Generalized quantiles as risk measures
- Recursive estimation of a failure probability for a Lipschitz function
- Multivariate quantiles and multiple-output regression quantiles: from \(L_{1}\) optimization to halfspace depth
- Quantile estimation with adaptive importance sampling
- Finding the minimum of a function
- Control Variates for Probability and Quantile Estimation
- Control Variates for Quantile Estimation
- Optimise importance sampling quantile estimation
- A Sturdy Reduced-Bias Extreme Quantile (VaR) Estimator
- Adaptive multilevel splitting: Historical perspective and recent results
- Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models
This page was built for publication: Deterministic computation of quantiles in a Lipschitz framework