Computing highly accurate confidence limits from discrete data using importance sampling

DOI10.1007/s11222-013-9409-1zbMath1325.62154OpenAlexW2109401020MaRDI QIDQ892812

Degui Li, Chris J. Lloyd

Publication date: 12 November 2015

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-013-9409-1

zbMATH Keywords

stochastic approximationRobbins-Monro algorithmprofile upper limits

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25) Stochastic approximation (62L20)

Related Items (2)

Random weighting-based quantile estimation via importance resampling ⋮ Conservative confidence intervals for the intraclass correlation coefficient for clustered binary data

Uses Software

• bootlib

Cites Work

• Unnamed Item
• A Newton-Raphson version of the multivariate Robbins-Monro procedure
• Improved Buehler limits based on refined designated statistics
• Handbook of Monte Carlo Methods
• On large deviations theory and asymptotically efficient Monte Carlo estimation
• Acceleration of Stochastic Approximation by Averaging
• Test Inversion Bootstrap Confidence Intervals
• SOME PROPERTIES OF PROFILE BOOTSTRAP CONFIDENCE INTERVALS
• Safe and Effective Importance Sampling
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Generating Monte Carlo Confidence Intervals by the Robbins-Monro Process
• Theory & Methods: Profile upper Confidence Limits from Discrete Data
• Methods of Reducing Sample Size in Monte Carlo Computations
• A Stochastic Approximation Method
• A Note on the Robbins-Monro Stochastic Approximation Method
• On a Stochastic Approximation Method

This page was built for publication: Computing highly accurate confidence limits from discrete data using importance sampling