Francesco Bartolucci
Mixture latent autoregressive models for longitudinal data.
Tue, 08/09/2011 - 15:01 — SomNambulAuthors: Francesco Bartolucci, Fulvia Pennoni, Silvia Bacci
Subjects: Statistics
AbstractMany relevant statistical and econometric models for the analysis of
longitudinal data include a latent process to account for the unobserved
heterogeneity between subjects in a dynamic fashion. Such a process may be
continuous (typically an AR(1)) or discrete (typically a Markov chain). In this
paper, we propose a model for longitudinal data which is based on a mixture of
AR(1) processes with different means and correlation coefficients, but with
equal variances.An investigation of the discriminant power and dimensionality of items used for assessing health condition of elderly people.
Fri, 08/20/2010 - 15:01 — SomNambulAuthors: Francesco Bartolucci, Giorgio d'Agostino, Giorgio E. Montanari
Subjects: Applications
AbstractWith reference to the questionnaire adopted within the Italian project
"Ulisse" to assess health condition of elderly people, we investigate two
important issues: discriminant power and actual number of dimensions measured
by the items composing the questionnaire. The adopted statistical approach is
based on the joint use of the latent class model and a multidimensional item
response theory model based on the 2PL parametrization. The latter allows us to
account for the different discriminant power of these items.Assessment of school performance through a multilevel latent Markov Rasch model.
Tue, 09/29/2009 - 15:21 — SomNambulAuthors: Francesco Bartolucci, Fulvia Pennoni, Giorgio Vittadini
Subjects: Applications
AbstractAn extension of the latent Markov Rasch model is described for the analysis
of binary longitudinal data with covariates when subjects are collected in
clusters, e.g. students clustered in classes. For each subject, the latent
process is used to represent the characteristic of interest (e.g. ability)
conditional on the effect of the cluster to which he/she belongs. The latter
effect is modeled by a discrete latent variable associated with each cluster.
For the maximum likelihood estimation of the model parameters we outline an EM
algorithm.
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