Estimation in the probit normal model for binary outcomes using the SAEM algorithm

DOI10.1016/j.csda.2008.11.024zbMath1452.62540OpenAlexW2078222922MaRDI QIDQ961280

Jean-Louis Foulley, Florence Jaffrézic, Cristian Meza

Publication date: 30 March 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.11.024

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Generalized linear models (logistic models) (62J12)

Related Items (4)

Bayesian binary regression with exponential power link ⋮ Lasso-type estimators for semiparametric nonlinear mixed-effects models estimation ⋮ Estimation in nonlinear mixed-effects models using heavy-tailed distributions ⋮ Generalized linear mixed models for correlated binary data with \(t\)-link

Uses Software

• WinBUGS
• SAS
• MIXOR
• MIXNO
• PROC NLMIXED
• glimmix

Cites Work

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• The Monte Carlo EM method for estimating multinomial probit latent variable models
• Integrated likelihood methods for eliminating nuisance parameters. (With comments and a rejoinder).
• A Monte Carlo EM method for estimating multinomial probit models.
• Convergence of a stochastic approximation version of the EM algorithm
• REML estimation for binary data in GLMMs
• Estimation in generalized linear models with random effects
• A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Laplace's approximation for nonlinear mixed models
• Bias Correction in Generalized Linear Mixed Models With Multiple Components of Dispersion
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models
• A type of restricted maximum likelihood estimator of variance components in generalised linear mixed models
• A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses
• Coupling a stochastic approximation version of EM with an MCMC procedure
• Approximate Inference in Generalized Linear Mixed Models
• A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration
• Bayesian inference for variance components using only error contrasts
• Bias correction in generalised linear mixed models with a single component of dispersion
• Estimation in generalised linear mixed models with binary outcomes by simulated maximum likelihood
• REML Estimation of Variance Parameters in Nonlinear Mixed Effects Models Using the SAEM Algorithm
• Bayesian Analysis of Binary and Polychotomous Response Data
• Ascent-Based Monte Carlo Expectation– Maximization
• Double Hierarchical Generalized Linear Models (With Discussion)
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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