English

Model selection in the space of Gaussian models invariant by symmetry

Statistics Theory 2022-05-17 v3 Mathematical Physics math.MP Representation Theory Statistics Theory

Abstract

We consider multivariate centered Gaussian models for the random variable Z=(Z1,,Zp)Z=(Z_1,\ldots, Z_p), invariant under the action of a subgroup of the group of permutations on {1,,p}\{1,\ldots, p\}. Using the representation theory of the symmetric group on the field of reals, we derive the distribution of the maximum likelihood estimate of the covariance parameter Σ\Sigma and also the analytic expression of the normalizing constant of the Diaconis-Ylvisaker conjugate prior for the precision parameter K=Σ1K=\Sigma^{-1}. We can thus perform Bayesian model selection in the class of complete Gaussian models invariant by the action of a subgroup of the symmetric group, which we could also call complete RCOP models. We illustrate our results with a toy example of dimension 44 and several examples for selection within cyclic groups, including a high dimensional example with p=100p=100.

Keywords

Cite

@article{arxiv.2004.03503,
  title  = {Model selection in the space of Gaussian models invariant by symmetry},
  author = {Piotr Graczyk and Hideyuki Ishi and Bartosz Kołodziejek and Hélène Massam},
  journal= {arXiv preprint arXiv:2004.03503},
  year   = {2022}
}

Comments

28 pages of the main text, 15 pages of the Supplementary material, 6 figures, 5 tables. Accepted to Annals of Statistics