Generating Monte Carlo Confidence Intervals by the Robbins-Monro Process

DOI10.2307/2347625zbMath0825.62989OpenAlexW1783995108MaRDI QIDQ4843989

Paul H. Garthwaite, Stephen T. Buckland

Publication date: 17 August 1995

Published in: Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/2347625

zbMATH Keywords

bootstrapMonte Carlo methodmark-recaptureconfidence intervalRobbins-Monro search

Mathematics Subject Classification ID

Applications of statistics (62P99)

Related Items (12)

Simultaneous adjustment of bias and coverage probabilities for confidence intervals ⋮ Using adaptive weighted least squares to reduce the lengths of confidence intervals ⋮ Importance accelerated Robbins-Monro recursion with applications to parametric confidence limits ⋮ Computing highly accurate confidence limits from discrete data using importance sampling ⋮ On an inferential model construction using generalized associations ⋮ Parametric bootstrapping with nuisance parameters ⋮ An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters ⋮ Exact confidence intervals generated by conditional parametric bootstrapping ⋮ Recent developments in bootstrap methodology ⋮ Confidence Intervals from Stochastic Approximation ⋮ Adaptive optimal scaling of Metropolis–Hastings algorithms using the Robbins–Monro process ⋮ Efficient Construction of Test Inversion Confidence Intervals Using Quantile Regression

Uses Software

• AS 259

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