Posterior convergence rate of a class of Dirichlet process mixture model for compositional data

被引：0

作者：

Barrientos, Andres F. ^{[1
]}

Jara, Alejandro ^{[2
]}

Wehrhahn, Claudia ^{[3
]}

机构：

[1] Duke Univ, Dept Stat Sci, Box 90251, Durham, NC 27708 USA

[2] Pontificia Univ Catolica Chile, Dept Stat, Casilla 306,Correo 22, Santiago, Chile

[3] Univ Nacl Autonoma Mexico, IIMAS, Dept Probabilidad & Estadist, Apartado Postal 20-126, Mexico City 01000, DF, Mexico

来源：

STATISTICS & PROBABILITY LETTERS | 2017年 / 120卷

基金：

美国国家科学基金会;

关键词：

Simplex; Bayesian nonparametrics; Frequentist asymptotics; BAYESIAN DENSITY-ESTIMATION;

D O I：

10.1016/j.spl.2016.09.008

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We propose a Dirichlet process mixture of mixtures of Dirichlet models for density estimation. By assuming random sampling from a density belonging to a Holder class, we show that the posterior distribution of the model is rate-optimal. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：45 / 51

页数：7

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