ePrints

Heavy-Tailed NGG-Mixture Models

Lookup NU author(s): Dr Vianey Palacios RamirezORCiD

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

Heavy tails are often found in practice, and yet they are an Achilles heel of a variety of mainstream random probability measures such as the Dirichlet process (DP). The first contribution of this paper focuses on characterizing the tails of the so-called normalized generalized gamma (NGG) process. We show that the right tail of an NGG process is heavy-tailed provided that the centering distribution is itself heavy-tailed; the DP is the only member of the NGG class that fails to obey this convenient property. A second contribution of the paper rests on the development of two classes of heavy-tailed mixture models and the assessment of their relative merits. Multivariate extensions of the proposed heavy-tailed mixtures are devised here, along with a predictor-dependent version, to learn about the effect of covariates on a multivariate heavy-tailed response. The simulation study suggests that the proposed method performs well in various scenarios, and we showcase the application of the proposed methods in a neuroscience dataset.

Publication metadata

Author(s): Palacios Ramírez Vianey, de Carvalho Miguel, Gutiérrez Luis

Publication type: Article

Publication status: Published

Journal: Bayesian Analysis

Year: 2024

Pages: epub ahead of print

Online publication date: 20/03/2024

Acceptance date: 01/01/2024

ISSN (print): 1936-0975

ISSN (electronic): 1931-6690

Publisher: International Society for Bayesian Analysis (ISBA)

URL: https://doi.org/10.1214/24-BA1420

DOI: 10.1214/24-BA1420

Altmetrics

Share