Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure

DOI10.1007/s11749-014-0397-zzbMath1315.62065arXiv1404.6386OpenAlexW2082478935MaRDI QIDQ2348716

Antonello Maruotti

Publication date: 15 June 2015

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.6386

zbMATH Keywords

longitudinal dataconditional maximum likelihoodhidden Markov chainsskin cancernon-ignorable missingness

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

Related Items (5)

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 ⋮ Mixture of multivariate \(t\) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values ⋮ A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error

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