Marginal likelihood estimation from the Metropolis output: tips and tricks for efficient implementation in generalized linear latent variable models
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Publication:5219477
DOI10.1080/00949655.2013.783580zbMath1453.62232OpenAlexW2019913086MaRDI QIDQ5219477
Irini Moustaki, Silia Vitoratou, Ioannis Ntzoufras
Publication date: 12 March 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2013.783580
Computational methods for problems pertaining to statistics (62-08) Generalized linear models (logistic models) (62J12)
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Cites Work
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- Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects
- Latent class models for mixed variables with applications in archaeometry
- Bayesian variable and link determination for generalised linear models
- Generalized latent trait models
- High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature
- Bayesian variable selection using cost-adjusted BIC, with application to cost-effective measurement of quality of health care
- Latent Variable Models and Factor Analysis
- Marginal Likelihood from the Gibbs Output
- Sampling-Based Approaches to Calculating Marginal Densities
- Marginal Likelihood Estimation via Power Posteriors
- The Calculation of Posterior Distributions by Data Augmentation
- A candidate's formula: A curious result in Bayesian prediction
- Batch Size Effects in the Analysis of Simulation Output
- Estimating Bayes Factors via Posterior Simulation With the Laplace-Metropolis Estimator
- Estimation of Generalized Linear Latent Variable Models
- Marginal Likelihood From the Metropolis–Hastings Output
- Bayes Factors
- Latent variable models for mixed categorical and survival responses, with an application to fertility preferences and family planning in Bangladesh
- Inference in Semiparametric Dynamic Models for Binary Longitudinal Data