Título: Mixed Effects State-Space Models for Longitudinal Data with Heavy Tails

Data: 21/07/2021
Horário: 15:30h
Local: Transmissão online

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Resumo: The mixed-effects state space models (MESSM) can be considered as an alternative to study the HIV dynamic in a longitudinal data environment, defining the mixed-effects component into state-space models setup. As in Liu et al., 2011, we consider a hierarchical structure to capture possible differences between the immune systems for different patients. We extend MESSM, allowing observational errors to follow a more flexible distribution to take account for heavy tails. Our proposal consists in defining the error distribution of the observations using the hierarchical structure of the scale mixture of normal distributions. Moreover, the mixing parameters obtained as a by-product of the scale mixture representation can be used to identify outliers. Under the Bayesian paradigm, an efficient Markov Chain Monte Carlo (MCMC) algorithm is implemented. To evaluate the properties of the proposed models, we carried out simulation studies. Finally, we illustrate our approach with an application in real HIV longitudinal data.

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