When is Eaton's Markov chain irreducible?

TL;DR

This paper provides a simple, necessary and sufficient condition to determine when Eaton's Markov chain is irreducible, based on an analysis of the underlying statistical model and prior, simplifying the verification process.

Contribution

It introduces a new, easily checked characterization of irreducibility for Eaton's Markov chain, aiding in the analysis of strong admissibility in statistical models.

Findings

Characterization of irreducibility for general state space Markov chains

Necessary and sufficient condition based on $P$ and $ u$

Application examples demonstrating the criterion

Abstract

Consider a parametric statistical model $P(\mathrm{d}x|\theta)$ and an improper prior distribution $\nu(\mathrm{d}\theta)$ that together yield a (proper) formal posterior distribution $Q(\mathrm{d}\theta|x)$. The prior is called strongly admissible if the generalized Bayes estimator of every bounded function of $\theta$ is admissible under squared error loss. Eaton [Ann. Statist. 20 (1992) 1147--1179] has shown that a sufficient condition for strong admissibility of $\nu$ is the local recurrence of the Markov chain whose transition function is $R(\theta,\mathrm{d}\eta)=\int Q(\mathrm{d}\eta|x)P(\mathrm {d}x|\theta)$. Applications of this result and its extensions are often greatly simplified when the Markov chain associated with $R$ is irreducible. However, establishing irreducibility can be difficult. In this paper, we provide a characterization of irreducibility for general state space Markov chains and use this characterization to develop an easily checked, necessary and sufficient condition for irreducibility of Eaton's Markov chain. All that is required to check this condition is a simple examination of $P$ and $\nu$. Application of the main result is illustrated using two examples.