Articulo
Bayesian density estimation for compositional data using random bernstein polynomials
Journal of Statistical Planning and Inference
Registro en:
1141193
1141193
Autor
Barrientos, Andrés F
Jara-Vallejos, Alejandro Antonio
Quintana, Fernando A
Institución
Resumen
We propose a Bayesian nonparametric procedure or density estimation for data in a d-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 t Regular FONDECYT FONDECYT