[en] 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.
Disciplines :
Mathematics
Author, co-author :
Lambert, Philippe ; Université de Liège - ULiège > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales
Eilers, Paul H.C.; Universiteit Utrecht > Faculty of Social and Behavioural Sciences
Language :
English
Title :
Bayesian density estimation from grouped continuous data
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