An importance sampling algorithm for exact conditional tests in log-linear models
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Publication:4267763
DOI10.1093/biomet/86.2.321zbMath0931.62057OpenAlexW2049785683MaRDI QIDQ4267763
James G. Booth, Ronald W. Butler
Publication date: 21 February 2000
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1093/biomet/86.2.321
Generalized linear models (logistic models) (62J12) Numerical analysis or methods applied to Markov chains (65C40)
Related Items (10)
The analysis of ordered categorical data: An overview and a survey of recent developments. (With discussion) ⋮ Exact inference in contingency tables via stochastic approximation Monte Carlo ⋮ Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities ⋮ Computing p-values in conditional independence models for a contingency table ⋮ Computing highly accurate or exact \(P\)-values using importance sampling ⋮ Markov bases for typical block effect models of two-way contingency tables ⋮ Sampling for Conditional Inference on Case–Control Data ⋮ Hybrid schemes for exact conditional inference in discrete exponential families ⋮ Sampling for Conditional Inference on Case–Control Data ⋮ Power of the Sequential Monte Carlo Test
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