A discrete time event‐history approach to informative drop‐out in mixed latent Markov models with covariates

DOI10.1111/biom.12224zbMath1419.62308arXiv1306.1678OpenAlexW1871253248WikidataQ41736847 ScholiaQ41736847MaRDI QIDQ3465726

Francesco Bartolucci, Alessio Farcomeni

Publication date: 22 January 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1306.1678

zbMATH Keywords

hidden Markov modelsexpectation-maximization algorithmdiscrete latent variablesshared-parameter models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Generalized linear models (logistic models) (62J12) Markov processes: estimation; hidden Markov models (62M05)

Related Items (10)

Bayesian analysis of latent Markov models with non-ignorable missing data ⋮ Joint models for a GLM-type longitudinal response and a time-to-event with smooth random effects ⋮ Multiple imputation of longitudinal categorical data through bayesian mixture latent Markov models ⋮ Generalized Linear Mixed Models Based on Latent Markov Heterogeneity Structures ⋮ Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers ⋮ Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: a simulation study ⋮ Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models ⋮ Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition ⋮ Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data ⋮ Joint mixture quantile regressions and time-to-event analysis

Uses Software

• JM

Cites Work

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