Bayesian density estimation from grouped continuous data

Grouped data occur frequently in practice, either because of limited resolution of
instruments, or because data have been summarized in relatively wide bins. A
combination of the composite link model with roughness penalties is proposed to
estimate smooth densities from such data in a Bayesian framework. A simulation
study is used to evaluate the performances of the strategy in the estimation of a
density, of its quantiles and rst moments. Two illustrations are presented: the rst
one involves grouped data of lead concentrations in the blood and the second one
the number of deaths due to tuberculosis in The Netherlands in wide age classes.