Clustering in linear mixed models with approximate Dirichlet process mixtures using EM algorithm
From MaRDI portal
Publication:4970799
DOI10.1177/1471082X12471372OpenAlexW2070855429MaRDI QIDQ4970799
Publication date: 7 October 2020
Published in: Statistical Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1177/1471082x12471372
EM algorithmlinear mixed modelslikelihood inferencestick breakingapproximate Dirichlet process mixture
Related Items
Additive mixed models with approximate Dirichlet process mixtures: the EM approach, Unsupervised learning of mixture regression models for longitudinal data, The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture, Recursive parameter estimation algorithm of the Dirichlet hidden Markov model, Fast approximation of variational Bayes Dirichlet process mixture using the maximization-maximization algorithm, An application of Dirichlet process in clustering subjects via variance shift models: A course-evaluation study, Tree-Structured Clustering in Fixed Effects Models, Mixture of linear mixed models using multivariatetdistribution, Considering the sample sizes as truncated Poisson random variables in mixed effects models
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Random-Effects Models for Longitudinal Data
- Multivariate generalized linear mixed models with semi-nonparametric and smooth nonparametric random effects densities
- Simultaneous curve registration and clustering for functional data
- Model-based clustering for longitudinal data
- Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution
- Bootstrap methods: another look at the jackknife
- Discreteness of Ferguson selections
- Ferguson distributions via Polya urn schemes
- Additive mixed models with Dirichlet process mixture and P-spline priors
- Clustering for multivariate continuous and discrete longitudinal data
- A Bayesian analysis of some nonparametric problems
- Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination
- Center-adjusted inference for a nonparametric Bayesian random effect distribution
- A Linear Mixed-Effects Model With Heterogeneity in the Random-Effects Population
- Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments
- Sampling the Dirichlet Mixture Model with Slices
- Clustering Using Objective Functions and Stochastic Search
- Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models
- Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
- A semiparametric Bayesian model for randomised block designs
- Approximating distributions of random functionals of Ferguson-Dirichlet priors
- A recursive algorithm for nonparametric analysis with missing data
- Estimating Normal Means with a Dirichlet Process Prior
- A Smooth Nonparametric Estimate of a Mixing Distribution Using Mixtures of Gaussians
- A Bayesian Population Model With Hierarchical Mixture Priors Applied to Blood Count Data
- Semiparametric Regression
- Bayesian Semiparametric Median Regression Modeling
- Clustering for Sparsely Sampled Functional Data
- Gibbs Sampling Methods for Stick-Breaking Priors
- Estimating normal means with a conjugate style dirichlet process prior
- Bayesian Density Estimation and Inference Using Mixtures
- A Simplex Method for Function Minimization
- Linear mixed models for longitudinal data
- Improved criteria for clustering based on the posterior similarity matrix
- Variational inference for Dirichlet process mixtures
