When is Eaton's Markov chain irreducible?

Details When is Eaton's Markov chain irreducible? When is Eaton's Markov chain irreducible?

2007-09-04

arxiv.org/abs/0709.0448v1

Bernoulli 2007, Vol. 13, No. 3, 641-652

Mathematics

Statistics Theory

Published at http://dx.doi.org/10.3150/07-BEJ6191 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statist

Scientific paper

10.3150/07-BEJ6191

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.

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