artículo
Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials
Date
2022Registration in:
10.1214/22-EJS2002
1935-7524
SCOPUS_ID:85128416045
WOS:000825293500045
Author
Wehrhahn, Claudia
Barrientos, Andrés F.
Jara Vallejos, Alejandro Antonio
Institutions
Abstract
We discuss Bayesian nonparametric procedures for the regression analysis of compositional responses, that is, data supported on a multivariate simplex. The procedures are based on a modified class of multivariate Bernstein polynomials and on the use of dependent stick-breaking processes. A general model and two simplified versions of the general model are discussed. Appealing theoretical properties such as continuity, association structure, support, and consistency of the posterior distribution are established. Additionally, we exploit the use of spike-and-slab priors for choosing the version of the model that best adapts to the complexity of the underlying true data-generating distribution. The performance of the proposed model is illustrated in a simulation study and in an application to solid waste data from Colombia.