Séminaire Statistique Sherbrooke Université de Sherbrooke, Faculté des sciences, local D4-2019
"9 septembre 2013 - 15:30
Séminaire Statistique Sherbrooke
Université de Sherbrooke, Faculté des sciences, local D4-2019
Bayesian nonparametric density estimation under length bias sampling
Theodoros Nicoleris, University of teh Aegean, Grèce
A new density estimation method in a Bayesian nonparametric framework is presented when recorded data are not coming directly from the distribution of interest, but from a length biased version. From a Bayesian perspective, efforts to computationally evaluate posterior quantities conditionally on length biased data were hindered by the inability to circumvent the problem of a normalizing constant. In this talk a novel Bayesian nonparametric approach to the length bias sampling problem is presented which circumvents the issue of the normalizing constant. Numerical illustrations as well as a real data example are presented and the estimator is compared against its frequentist counterpart, the kernel density estimator for indirect data.
Exposé en français. Travail conjoint avec : a) S.J. Hatjispyros, University of the Aegean, Grèce b) Stephen Walker, University of Texas at Austin, États-Unis"