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September 2018 Some Aspects of Symmetric Gamma Process Mixtures
Zacharie Naulet, Éric Barat
Bayesian Anal. 13(3): 703-720 (September 2018). DOI: 10.1214/17-BA1058

Abstract

In this article, we present some specific aspects of symmetric Gamma process mixtures for use in regression models. First we propose a new Gibbs sampler for simulating the posterior. The algorithm is tested on two examples, the mean regression problem with normal errors, and the reconstruction of two dimensional CT images. In a second time, we establish posterior rates of convergence related to the mean regression problem with normal errors. For location-scale and location-modulation mixtures the rates are adaptive over Hölder classes, and in the case of location-modulation mixtures are nearly optimal.

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Zacharie Naulet. Éric Barat. "Some Aspects of Symmetric Gamma Process Mixtures." Bayesian Anal. 13 (3) 703 - 720, September 2018. https://doi.org/10.1214/17-BA1058

Information

Published: September 2018
First available in Project Euclid: 7 October 2017

zbMATH: 06989964
MathSciNet: MR3807863
Digital Object Identifier: 10.1214/17-BA1058

Subjects:
Primary: 62G08 , 62G20
Secondary: 60G57

Keywords: Bayesian nonparameterics , Nonparametric regression , signed random measures

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Vol.13 • No. 3 • September 2018
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