Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models
From MaRDI portal
Publication:2666058
DOI10.1007/s11749-020-00727-xzbMath1474.62208OpenAlexW3045127905MaRDI QIDQ2666058
Alessio Farcomeni, Sara Viviani, Monia Ranalli
Publication date: 22 November 2021
Published in: Test (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11749-020-00727-x
Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)
Related Items (2)
Causal inference for time-varying treatments in latent Markov models: an application to the effects of remittances on poverty dynamics ⋮ Parsimonious Hidden Markov Models for Matrix-Variate Longitudinal Data
Uses Software
Cites Work
- Unnamed Item
- On computing the distribution function for the Poisson binomial distribution
- Prognostic assessment of repeatedly measured time-dependent biomarkers, with application to dilated cardiomyopathy
- OCLUS: an analytic method for generating clusters with known overlap
- Clustering financial time series: new insights from an extended hidden Markov model
- Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models
- A multivariate hidden Markov model for the identification of sea regimes from incomplete skewed and circular time series
- Item response theory for longitudinal data: population parameter estimation
- Sparse logistic principal components analysis for binary data
- Principal component analysis of binary data by iterated singular value decomposition
- Asymptotic expansions for sums of nonidentically distributed Bernoulli random variables
- Clustering of multivariate binary data with dimension reduction via \(L_{1}\)-regularized likelihood maximization
- Dynamic mixtures of factor analyzers to characterize multivariate air pollutant exposures
- Dimensionality reduction for binary data through the projection of natural parameters
- Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure
- Covariate adjusted functional principal components analysis for longitudinal data
- Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition
- Properties of principal component methods for functional and longitudinal data analysis
- Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates
- Latent Markov Models for Longitudinal Data
- Advances in Principal Component Analysis
- Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures
- A discrete time event‐history approach to informative drop‐out in mixed latent Markov models with covariates
- A factor mixture analysis model for multivariate binary data
- Semiparametric estimation of time-dependent ROC curves for longitudinal marker data
- Hidden Markov latent variable models with multivariate longitudinal data
- Penalized estimation in latent Markov models, with application to monitoring serum calcium levels in end‐stage kidney insufficiency
- Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity
This page was built for publication: Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models
