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2020 On the Letac-Massam conjecture and existence of high dimensional Bayes estimators for graphical models
Emanuel Ben-David, Bala Rajaratnam
Electron. J. Statist. 14(1): 580-604 (2020). DOI: 10.1214/19-EJS1669


The Wishart distribution defined on the open cone of positive-definite matrices plays a central role in multivariate analysis and multivariate distribution theory. Its domain of parameters is often referred to as the Gindikin set. In recent years, varieties of useful extensions of the Wishart distribution have been proposed in the literature for the purposes of studying Markov random fields and graphical models. In particular, generalizations of the Wishart distribution, referred to as Type I and Type II (graphical) Wishart distributions introduced by Letac and Massam in Annals of Statistics (2007) play important roles in both frequentist and Bayesian inference for Gaussian graphical models. These distributions have been especially useful in high-dimensional settings due to the flexibility offered by their multiple-shape parameters. Concerning Type I and Type II Wishart distributions, a conjecture of Letac and Massam concerns the domain of multiple-shape parameters of these distributions. The conjecture also has implications for the existence of Bayes estimators corresponding to these high dimensional priors. The conjecture, which was first posed in the Annals of Statistics, has now been an open problem for about 10 years. In this paper, we give a necessary condition for the Letac and Massam conjecture to hold. More precisely, we prove that if the Letac and Massam conjecture holds on a decomposable graph, then no two separators of the graph can be nested within each other. For this, we analyze Type I and Type II Wishart distributions on appropriate Markov equivalent perfect DAG models and succeed in deriving the aforementioned necessary condition. This condition in particular identifies a class of counterexamples to the conjecture.


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Emanuel Ben-David. Bala Rajaratnam. "On the Letac-Massam conjecture and existence of high dimensional Bayes estimators for graphical models." Electron. J. Statist. 14 (1) 580 - 604, 2020.


Received: 1 February 2019; Published: 2020
First available in Project Euclid: 28 January 2020

zbMATH: 07163267
MathSciNet: MR4056267
Digital Object Identifier: 10.1214/19-EJS1669

Primary: 60E05 , 62-09
Secondary: 62F99

Keywords: Gaussian distribution , graphical models , Wishart distribution


Vol.14 • No. 1 • 2020
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