Finite mixture model of hidden Markov regression with covariate dependence
From MaRDI portal
Publication:6543902
DOI10.1002/sta4.469MaRDI QIDQ6543902
Publication date: 27 May 2024
Published in: Stat (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Parsimonious Hidden Markov Models for Matrix-Variate Longitudinal Data
- Decision boundaries for mixtures of regressions
- Hidden Markov models with mixtures as emission distributions
- Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
- Estimating the dimension of a model
- Identifiability of models for clusterwise linear regression
- Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering
- Model-based clustering via linear cluster-weighted models
- Matrix normal cluster-weighted models
- Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models
- Multivariate response and parsimony for Gaussian cluster-weighted models
- Multivariate cluster weighted models using skewed distributions
- Finite mixture models
- Flexible mixture modelling with the polynomial Gaussian cluster-weighted model
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
- Matrix-variate time series modelling with hidden Markov models
- Challenges in model-based clustering
- Longitudinal analysis of self-reported health status by mixture latent auto-regressive models
Related Items (1)
This page was built for publication: Finite mixture model of hidden Markov regression with covariate dependence