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Activity Number: 613
Type: Contributed
Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #312891 View Presentation
Title: Geometric Ergodicity of Bayesian Scale-Usage Models
Author(s): Andrew Olsen*+ and Radu Herbei
Companies: and Ohio State University
Keywords: MCMC ; Geometric Ergodicity ; Scale-usage
Abstract:

Geometric ergodicity is a key property of Markov chains that is typically used for establishing corresponding central limit theorems. However, it is often incredibly challenging to verify, particularly for sophisticated applications. Bayesian scale-usage models are utilized in areas such as survey data where each individual has a unique ranking system that is comparable to other individuals only through shifting and scaling their latent responses. In this work we study the ergodic properties of Markov chains exploring the posterior distribution corresponding to a general class of scale-usage models. We show that for such applications, under certain conditions, typical MCMC samplers are geometrically ergodic.


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