Random effect models for multivariate mixed data: A Parafac-based finite mixture approach
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Publication:6078167
DOI10.1177/1471082x211037405MaRDI QIDQ6078167
Marco Alfo', Paolo E. Giordani
Publication date: 27 September 2023
Published in: Statistical Modelling (Search for Journal in Brave)
Cites Work
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- Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects
- A flexible approach to finite mixture regression models for multivariate mixed responses
- Multivariate random effect models with complete and incomplete data
- The geometry of mixture likelihoods, part II: The exponential family
- The geometry of mixture likelihoods: A general theory
- Convergence of Simar's algorithm for finding the maximum likelihood estimate of a compound Poisson process
- Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
- Maximum likelihood estimation of a compound Poisson process
- Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics
- Estimating the dimension of a model
- A mixture likelihood approach for generalized linear models
- Testing the impossible: identifying exclusion restrictions
- Finite mixtures of multivariate Poisson distributions with application
- Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
- Semiparametric mixture models for multivariate count data, with application
- Imputation of Mixed Data With Multilevel Singular Value Decomposition
- Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
- Estimating Mixtures of Normal Distributions and Switching Regressions
- A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models
- Simulated maximum likelihood estimation of multivariate mixed‐Poisson regression models, with application
- Multivariate generalized linear mixed models with random intercepts to analyze cardiovascular risk markers in type-1 diabetic patients
- Nonparametric Bayes Modeling of Multivariate Categorical Data
- Applied Multiway Data Analysis
- A New Approach to Estimating Switching Regressions
- A new look at the statistical model identification
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