Grouped Heterogeneous Mixture Modeling for Clustered Data

DOI10.1080/01621459.2020.1777136zbMath1464.62329arXiv1804.00888OpenAlexW3033605812MaRDI QIDQ4999176

Shonosuke Sugasawa

Publication date: 6 July 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1804.00888

zbMATH Keywords

EM algorithmmaximum likelihood estimationmixture of expertsunobserved heterogeneityfinite mixture

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (3)

Grouped Generalized Estimating Equations for Longitudinal Data Analysis ⋮ Heterogeneous analysis for clustered data using grouped finite mixture models ⋮ Unnamed Item

Uses Software

• lme4

Cites Work

• Unnamed Item
• Unnamed Item
• Smoothly mixing regressions
• Generalized smooth finite mixtures
• Mixed effects regression trees for clustered data
• Multi-species distribution modeling using penalized mixture of regressions
• Linear and generalized linear mixed models and their applications.
• Estimating the dimension of a model
• Testing the order of a model using locally conic parametrization: Population mixtures and stationary ARMA processes
• Mixture models for clustering multilevel growth trajectories
• Laplace mixture of linear experts
• Latent mixture modeling for clustered data
• Regression density estimation using smooth adaptive Gaussian mixtures
• Mixed Models
• The Nested Dirichlet Process
• Nonparametric Bayes Conditional Distribution Modeling With Variable Selection
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Hierarchical Dirichlet Processes
• PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA
• Variable Selection in Finite Mixture of Regression Models
• Inference in Generalized Additive Mixed Models by Using Smoothing Splines
• Modeling Schizophrenic Behavior Using General Mixture Components
• Conditional Second-Order Generalized Estimating Equations for Generalized Linear and Nonlinear Mixed-Effects Models
• Mixtures of marginal models
• Finite mixture models
• Grouped Patterns of Heterogeneity in Panel Data
• Bootstrapping Robust Estimates for Clustered Data
• Multivariate Bernoulli Mixture Models with Application to Postmortem Tissue Studies in Schizophrenia
• Maximum Likelihood Estimation of Misspecified Models

This page was built for publication: Grouped Heterogeneous Mixture Modeling for Clustered Data