A Metropolis algorithm to obtain co-sufficient samples with applications in conditional tests

DOI10.1080/03610918.2018.1458140OpenAlexW2802376287WikidataQ129894472 ScholiaQ129894472MaRDI QIDQ5087540

Nelson L. Souza Filho, James D. Santos

Publication date: 1 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2018.1458140

zbMATH Keywords

Metropolis-Hastings algorithmconditional testco-sufficient sample

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Computational methods for problems pertaining to operations research and mathematical programming (90-08) Sufficient statistics and fields (62B05)

Related Items (1)

Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling

Uses Software

• BayesDA

Cites Work

• Exact conditional tests and approximate bootstrap tests for the von Mises distribution
• A note on Moore's conjecture
• On goodness of fit tests for the Poisson, negative binomial and binomial distributions
• Use of the Gibbs Sampler to Obtain Conditional Tests, with Applications
• Miscellanea. Stochastic simulations conditioned on sufficient statistics
• On the Conditional Distribution of Goodness-of-Fit Tests
• Monte Carlo conditioning on a sufficient statistic
• A note on Srinivasan's goodness-of-fit test
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: A Metropolis algorithm to obtain co-sufficient samples with applications in conditional tests