Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1148085 | Journal of Statistical Planning and Inference | 2015 | 10 Pages |
Abstract
We propose a Bayesian nonparametric procedure for density estimation for data in a dd-dimensional simplex. To this aim, we propose a prior distribution on probability measures based on a modified class of multivariate Bernstein polynomials. The model for the probability distribution corresponds to a mixture of Dirichlet distributions, with random weights and a random number of components. Theoretical properties of the proposal are provided, including posterior consistency and concentration rates of the posterior distribution.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Andrés F. Barrientos, Alejandro Jara, Fernando A. Quintana,