# A hierarchical latent class model for predicting disability small area counts from survey data

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*schedule*
le vendredi 30 mars 2018 de 10h00 à 11h00

**Organisé par :**Castillo, Fischer, Giulini, Gribkova, Levrard, Roquain, Sangnier

**Intervenant :**Maria Giovanna Ranalli (Università degli Studi di Perugia)

**Lieu :**UPMC, salle 15-16.201

**Sujet :**A hierarchical latent class model for predicting disability small area counts from survey data

**Résumé :**

We consider the estimation of the number of severely
disabled people by using data from the Italian survey on ‘Health
conditions and appeal to Medicare’. In this survey, disability is
indirectly measured by using a set of categorical items, which
consider a set of functions concerning the ability of a person to
accomplish everyday tasks. Latent class models can be employed to
classify the population according to different levels of a latent
variable connected with disability. The survey is designed to
provide reliable estimates at the level of administrative regions,
whereas local authorities are interested in quantifying the number
of people who belong to each latent class at a subregional level.
Therefore, small area estimation techniques should be used. The
challenge is that the variable of interest is not observed.
Adopting a full Bayesian approach, we base small area estimation
on a latent class model in which the probability of belonging to
each latent class changes with covariates and the influence of age
is learnt from the data by using penalized splines.
Demmler–Reinsch bases are shown to improve speed and mixing of
Markov chain Monte Carlo chains used to simulate posteriors.

Joint work with Enrico Fabrizi and Giorgio E. Montanari.