A flexible approach for multivariate mixed-effects models with non-ignorable missing values
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Publication:5220937
DOI10.1080/00949655.2015.1005014OpenAlexW2015116977MaRDI QIDQ5220937
Wei Liu, Juxin Liu, Guohua Yan, Lang Wu
Publication date: 27 March 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2015.1005014
random effectsDirichlet processDirichlet process mixture modelsBayesian MCMCnon-ignorable missing values
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- Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies
- Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
- Nonparametric Bayesian data analysis
- Nonparametric hierarchical Bayes via sequential imputations
- A Bayesian analysis of some nonparametric problems
- Misspecified maximum likelihood estimates and generalised linear mixed models
- Center-adjusted inference for a nonparametric Bayesian random effect distribution
- A longitudinal study of children's aggressive behaviours based on multivariate mixed models with incomplete data
- Bayesian Nonparametric Inference for Random Distributions and Related Functions
- Sequential importance sampling for nonparametric Bayes models: The next generation
- Estimating Normal Means with a Dirichlet Process Prior
- Markov chain Monte Carlo in approximate Dirichlet and beta two-parameter process hierarchical models
- Bayesian Measures of Model Complexity and Fit
- A SEMIPARAMETRIC BAYESIAN APPROACH TO NETWORK MODELLING USING DIRICHLET PROCESS PRIOR DISTRIBUTIONS
- Variational inference for Dirichlet process mixtures
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