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

@article{Maruotti2014HandlingND,
  title={Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure},
  author={Antonello Maruotti},
  journal={TEST},
  year={2014},
  volume={24},
  pages={84-109}
}
We illustrate a class of conditional models for the analysis of longitudinal data suffering attrition in random effects models framework, where the subject-specific random effects are assumed to be discrete and to follow a time-dependent latent process. The latent process accounts for unobserved heterogeneity and correlation between individuals in a dynamic fashion, and for dependence between the observed process and the missing data mechanism. Of particular interest is the case where the… 
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