This paper considers random coefficients binary choice models. The main goal is to estimate the density of the random coefficients nonparametrically. This is an ill-posed inverse problem characterized by an integral transform. A new density estimator for the random coefficients is developed,...

This paper deals with nonparametric estimation of conditional densities in mixture models. The proposed approach consists to perform a preliminary clustering algorithm to guess the mixture component of each observation. Conditional densities of the mixture model are then estimated using kernel...