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On Cholesky structures on real symmetric matrices and their applications

By Hideyuki Ishi

Appears in collection : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

As a generalization of fill-in free property of a sparse positive definite real symmetric matrix with respect to the Cholesky decomposition, we introduce a notion of (quasi-)Cholesky structure for a real vector space of symmetric matrices. The cone of positive definite symmetric matrices in a vector space with a quasi-Cholesky structure admits explicit calculations and rich analysis similar to the ones for Gaussian selsction model associated to a decomposable graph. In particular, we can apply our method to a decomposable graphical model with a vertex pemutation symmetry.

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Citation data

  • DOI 10.24350/CIRM.V.19641103
  • Cite this video Ishi, Hideyuki (04/06/2020). On Cholesky structures on real symmetric matrices and their applications. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19641103
  • URL https://dx.doi.org/10.24350/CIRM.V.19641103

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Bibliography

  • HØJSGAARD, Søren et LAURITZEN, Steffen L. Graphical Gaussian models with edge and vertex symmetries. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2008, vol. 70, no 5, p. 1005-1027. - https://doi.org/10.1111/j.1467-9868.2008.00666.x
  • ISHI Hideyuki. Homogeneous cones and their applications to statistics, in Modern Methods of Multivariate Statistics, p. 135-154, Hermann, 2014.
  • LETAC, Gérard, MASSAM, Hélène, et al. Wishart distributions for decomposable graphs. The Annals of Statistics, 2007, vol. 35, no 3, p. 1278-1323. - https://www.jstor.org/stable/25463600
  • MASSAM, H., LI, Q., et GAO, X. Bayesian precision and covariance matrix estimation for graphical Gaussian models with edge and vertex symmetries. Biometrika, 2018, vol. 105, no 2, p. 371-388. - https://doi.org/10.1093/biomet/asx084

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