Convergence and accuracy of Gibbs sampling for conditional distributions in generalized linear models
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Publication:1807176
DOI10.1214/aos/1018031104zbMath0932.62078OpenAlexW1925309478MaRDI QIDQ1807176
Publication date: 9 November 1999
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aos/1018031104
Asymptotic distribution theory in statistics (62E20) Generalized linear models (logistic models) (62J12) Numerical analysis or methods applied to Markov chains (65C40)
Cites Work
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- Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms
- On the relationship between two asymptotic expansions for the distribution of sample mean and its applications
- Saddlepoint approximation at the edges of a conditional sample space
- Inference from iterative simulation using multiple sequences
- Algebraic algorithms for sampling from conditional distributions
- Markov chains for exploring posterior distributions. (With discussion)
- General Irreducible Markov Chains and Non-Negative Operators
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- The Calculation of Posterior Distributions by Data Augmentation
- Saddlepoint expansions for conditional distributions
- Computing Distributions for Exact Logistic Regression
- Saddle point approximation for the distribution of the sum of independent random variables
- The modified signed likelihood statistic and saddlepoint approximations
- Approximate Conditional Inference in Exponential Families Via the Gibbs Sampler
- Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
- Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions.
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