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Activity Number: 246 - Bayesian Nonparametrics
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #304486
Title: A Bayesian Nonparametric Model for Upper Record Data
Author(s): Joon Jin Song* and Jung-In Seo
Companies: Baylor University and Daejeon University
Keywords: Blocked Gibbs Sampling; Dirichlet Process Mixture; Upper Record Value; Pareto Distribution; Nonparametric Bayesian Analysis

This paper proposes a nonparametric Bayesian approach using a Dirichlet process mixture model with Pareto kernels to estimate the density of observed upper record values and predict future upper record values. A reference distribution in the nonparametric Bayesian model is provided on the basis of an objective prior for unknown parameters of the Pareto distribution to avoid difficulties caused by finding values of hyperparameters in the reference distribution. For the posterior computation, a blocked Gibbs sampling is provided for the proposed model. Finally, the proposed approach is illustrated with analyzing two real data sets representing the record values of average annual temperatures and carbon dioxide emissions.

Authors who are presenting talks have a * after their name.

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