Bayesian Nonparametric Density Estimation under Length Bias
Abstract
A 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 paper we present a novel Bayesian nonparametric approach to the length bias sampling problem 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 of Jones (1991).
Cite
@article{arxiv.1510.06307,
title = {Bayesian Nonparametric Density Estimation under Length Bias},
author = {Spyridon J. Hatjispyros and Theodoros Nicoleris and Stephen G. Walker},
journal= {arXiv preprint arXiv:1510.06307},
year = {2015}
}