Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials

DOI10.1214/22-EJS2002OpenAlexW3196505561MaRDI QIDQ2137796

Claudia Wehrhahn, Andrés F. Barrientos, Alejandro Jara

Publication date: 11 May 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.13403

zbMATH Keywords

Dirichlet processdensity regressiondependent Dirichlet processesfully nonparametric regression

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• CODA
• spBayes
• BayesDA

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Posterior consistency in conditional distribution estimation
• Bayesian inference with dependent normalized completely random measures
• Vectors of two-parameter Poisson-Dirichlet processes
• Bayesian density estimation for compositional data using random Bernstein polynomials
• Dirichlet component regression and its applications to psychiatric data
• Two-dimensional Bernstein polynomial density estimators
• Slice sampling. (With discussions and rejoinder)
• Markov chains for exploring posterior distributions. (With discussion)
• Adaptive-modal Bayesian nonparametric regression
• Asymptotic properties of Bernstein estimators on the simplex
• The dependent Dirichlet process and related models
• Bayesian nonparametric data analysis
• Smooth estimation of a distribution and density function on a hypercube using Bernstein polynomials for dependent random vectors
• On the support of MacEachern's dependent Dirichlet processes and extensions
• The multivariate beta process and an extension of the Polya tree model
• A class of mixtures of dependent tail-free processes
• Logistic-Normal Distributions: Some Properties and Uses
• A Predictive Approach to Model Selection
• Bayesian density estimation using bernstein polynomials
• On M-ancillarity
• Consistency of Bernstein Polynomial Posteriors
• Gibbs Sampling Methods for Stick-Breaking Priors
• Random Bernstein Polynomials
• Non-parametric regression for compositional data
• Nonparametric Bayesian inference for the spectral density function of a random field
• On Bayes procedures

This page was built for publication: Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials