Flexible random intercept models for binary outcomes using mixtures of normals
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Publication:1020200
DOI10.1016/j.csda.2006.09.031zbMath1445.62191OpenAlexW2048157682WikidataQ36076331 ScholiaQ36076331MaRDI QIDQ1020200
Brian S. Caffo, Ming-Wen An, Charles A. Rohde
Publication date: 29 May 2009
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc2031853
Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)
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Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Bayesian analysis of the multinomial probit model using marginal data augmentation
- Longitudinal data analysis using generalized linear models
- Matching conditional and marginal shapes in binary random intercept models using a bridge distribution function
- Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies
- A Monte Carlo EM method for estimating multinomial probit models.
- Conditional and marginal models: another view (with comments and rejoinder)
- Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
- Models for Longitudinal Data: A Generalized Estimating Equation Approach
- Marginalized Binary Mixed-Effects Models with Covariate-Dependent Random Effects and Likelihood Inference
- Analysis of multivariate probit models
- Statistical Evidence
- Numerical Analysis for Statisticians
- Simultaneously Modeling Joint and Marginal Distributions of Multivariate Categorical Responses
- Marginal Regression Models for Clustered Ordinal Measurements
- A Smooth Nonparametric Estimate of a Mixing Distribution Using Mixtures of Gaussians
- A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models
- Approximate Inference in Generalized Linear Mixed Models
- Mixed Models
- On the Use of the Quasi-Likelihood Method in Teratological Experiments
- Bayesian Analysis of Binary and Polychotomous Response Data
- On Information and Sufficiency
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