Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers
From MaRDI portal
Publication:1658183
DOI10.1016/j.csda.2016.05.024zbMath1464.62128OpenAlexW2408832908MaRDI QIDQ1658183
Antonello Maruotti, Antonio Punzo
Publication date: 14 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://eprints.soton.ac.uk/396818/2/manuscript.pdf
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Markov processes: estimation; hidden Markov models (62M05)
Related Items (13)
Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions ⋮ A new look at the inverse Gaussian distribution with applications to insurance and economic data ⋮ 2nd special issue on robust analysis of complex data ⋮ Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model ⋮ Extending finite mixtures of \(t\) linear mixed-effects models with concomitant covariates ⋮ Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies ⋮ Multivariate hidden semi-Markov models for longitudinal data: a dynamic regression modeling ⋮ Multiple scaled contaminated normal distribution and its application in clustering ⋮ Asymmetric clusters and outliers: mixtures of multivariate contaminated shifted asymmetric Laplace distributions ⋮ Mixture of multivariate \(t\) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values ⋮ Least squares moment identification of binary regression mixture models ⋮ Mixtures of multivariate contaminated normal regression models ⋮ Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Robust fitting of mixture regression models
- Manly transformation in finite mixture modeling
- Model-based clustering and classification with non-normal mixture distributions
- Decision boundaries for mixtures of regressions
- Clustering bivariate mixed-type data via the cluster-weighted model
- Maximum likelihood estimation of mark-recapture-recovery models in the presence of continuous covariates
- Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series
- Assessing the pattern of covariance matrices via an augmentation multiple testing procedure
- S-estimation of hidden Markov models
- Flexible mixture modeling via the multivariate \(t\) distribution with the Box-Cox transformation: an alternative to the skew-\(t\) distribution
- Quantile regression for longitudinal data based on latent Markov subject-specific parameters
- Analysis of time series subject to changes in regime
- Finite mixtures of multivariate skew \(t\)-distributions: some recent and new results
- Cluster-weighted \(t\)-factor analyzers for robust model-based clustering and dimension reduction
- Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
- Direct maximization of the likelihood of a hidden Markov model
- The dip test of unimodality
- Maximum-likelihood estimation for hidden Markov models
- A Markov model for switching regressions
- Estimating the dimension of a model
- Identifiability of models for clusterwise linear regression
- Model-based clustering via linear cluster-weighted models
- Panel data analysis: a survey on model-based clustering of time series
- Clustering and classification via cluster-weighted factor analyzers
- Finite mixtures of unimodal beta and gamma densities and the \(k\)-bumps algorithm
- Non-homogeneous hidden Markov-switching models for wind time series
- The generalized linear mixed cluster-weighted model
- A review of robust clustering methods
- Robust clusterwise linear regression through trimming
- Finite mixture and Markov switching models.
- Maximum likelihood estimation for multivariate skew normal mixture models
- Multivariate longitudinal data analysis with mixed effects hidden Markov models
- Parsimonious mixtures of multivariate contaminated normal distributions
- Multivariatetlinear mixed models for irregularly observed multiple repeated measures with missing outcomes
- Mixed Hidden Markov Models for Longitudinal Data: An Overview
- Latent Markov Models for Longitudinal Data
- Maximum likelihood estimation via the ECM algorithm: A general framework
- Model-Based Clustering of Non-Gaussian Panel Data Based on Skew-tDistributions
- A discrete time event‐history approach to informative drop‐out in mixed latent Markov models with covariates
- Skewness and mixtures of normal distributions
- Robust Estimation of the Mean and Covariance Matrix from Data with Missing Values
- An Application of the Laplace Method to Finite Mixture Distributions
- Numerical Maximisation of Likelihood: A Neglected Alternative to EM?
- Flexible mixture modelling with the polynomial Gaussian cluster-weighted model
- Robust fitting of hidden Markov regression models under a longitudinal setting
- Mixture of linear mixed models using multivariatetdistribution
- Hypothesis Testing for Mixture Model Selection
- A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure
- A mixture of generalized hyperbolic distributions
- On the Spectral Decomposition in Normal Discriminant Analysis
- Identifiability of Finite Mixtures of Elliptical Distributions
- Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
- Closed Likelihood Ratio Testing Procedures to Assess Similarity of Covariance Matrices
- Hidden Markov Models for Time Series
This page was built for publication: Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers
