The Role of Posterior Densities in Latent Variable Models for Ordinal Data

DOI10.1080/03610926.2013.810266zbMath1287.62013OpenAlexW2011027627MaRDI QIDQ5419316

Silvia Cagnone, Silvia Bianconcini

Publication date: 6 June 2014

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2013.810266

zbMATH Keywords

EM algorithmLaplace approximationsinternal approximationsadaptive Gauss Hermite

Mathematics Subject Classification ID

Cites Work

• Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects
• Asymptotic properties of adaptive maximum likelihood estimators in latent variable models
• Estimation of generalized linear latent variable models via fully exponential Laplace approximation
• The asymptotic posterior normality of the latent trait for polytomous IRT models
• Accuracy of Laplace approximation for discrete response mixed models
• Latent variable models for ordinal data by using the adaptive quadrature approximation
• Fully Exponential Laplace Approximations for the Joint Modelling of Survival and Longitudinal Data
• Applications of a Method for the Efficient Computation of Posterior Distributions
• Estimation of Generalized Linear Latent Variable Models
• Measures of multivariate skewness and kurtosis with applications
• Unnamed Item

This page was built for publication: The Role of Posterior Densities in Latent Variable Models for Ordinal Data