An automated (Markov chain) Monte Carlo EM algorithm
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Publication:4673864
DOI10.1080/0094965031000147704zbMath1060.62026OpenAlexW2040790268MaRDI QIDQ4673864
Juanjuan Fan, Richard A. Levine
Publication date: 9 May 2005
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/0094965031000147704
importance samplingrenewal theoryGibbs samplerMetropolis-Hastings algorithmgeneralized linear mixed modelsregenerative simulationsalamander data
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- On the convergence properties of the EM algorithm
- Monte Carlo EM with importance reweighting and its applications in random effects models.
- Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
- Maximum Likelihood Variance Components Estimation for Binary Data
- Maximum Likelihood Estimation for Probit-Linear Mixed Models with Correlated Random Effects
- Maximum Likelihood Algorithms for Generalized Linear Mixed Models
- Regeneration in Markov Chain Samplers
- Convergence controls for MCMC algorithms, with applications to hidden markov chains
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