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stat.ME2018
Modelling heterogeneity in Latent Space Models for Multidimensional Networks
Silvia D'Angelo, Marco Alfò, Thomas Brendan Murphy
Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual-specific features, which may vary across the networ…
stat.ME2018
A non-homogeneous hidden Markov model for partially observed longitudinal responses
Maria Francesca Marino, Marco Alfo'
Dropout represents a typical issue to be addressed when dealing with longitudinal studies. If the mechanism leading to missing information is non-ignorable, inference based on the…
stat.ME2017
A bi-dimensional finite mixture model for longitudinal data subject to dropout
Alessandra Spagnoli, Maria Francesca Marino, Marco Alfò
In longitudinal studies, subjects may be lost to follow-up, or miss some of the planned visits, leading to incomplete response sequences. When the probability of non-response, cond…