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

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.