Bayesian density estimation for compositional data using random Bernstein polynomials

DOI10.1016/j.jspi.2015.01.006zbMath1394.62037OpenAlexW1971418261MaRDI QIDQ899544

Fernando A. Quintana, Alejandro Jara, Andrés F. Barrientos

Publication date: 29 December 2015

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10533/228502

zbMATH Keywords

Bayesian nonparametricsDirichlet processsimplexrandom Bernstein polynomials

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Bayesian inference (62F15)

Related Items (9)

Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials ⋮ Consistency of Bayesian inference for multivariate max-stable distributions ⋮ Posterior convergence rate of a class of Dirichlet process mixture model for compositional data ⋮ Bernstein polynomial angular densities of multivariate extreme value distributions ⋮ Theory and computations for the Dirichlet process and related models: an overview ⋮ Bayesian nonparametrics for directional statistics ⋮ Bayesian Nonparametric Biostatistics ⋮ Asymptotic properties of Bernstein estimators on the simplex ⋮ Regression-type analysis for multivariate extreme values

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