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MONIA LUPPARELLI - University of Bologna, Italy

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” Hidden Markov chains for modeling unobserved heterogeneity in multilevel longitudinal data”
30 August 2012 from 4:00 PM to 5:00 PM
111 Tyson Bldg.
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In the context of multilevel longitudinal data, where sample units are collected
in clusters, an important aspect that should be accounted for is the unobserved
heterogeneity between sample units and between clusters. For this aim we propose
an approach based on nested hidden (latent) Markov chains, which are associated
to every sample unit and to every cluster. The approach allows us to account for the    
mentioned forms of unobserved heterogeneity in a dynamic fashion; it also allows
us to account for the correlation which may arise between the responses provided
by the units belonging to the same cluster. Given the complexity in computing
the manifest distribution of these response variables, we make inference on the
proposed model through a composite likelihood function based on all the possible
pairs of subjects within every cluster. The proposed approach is illustrated through
an application to a dataset concerning a sample of Italian workers in which a binary
response variable for the worker receiving an illness benefit was repeatedly observed.

(Joint work with Francesco Bartolucci)

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