2021
DOI: 10.48550/arxiv.2108.13403
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Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials

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… Show more

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