Bernoulli

  • Bernoulli
  • Volume 23, Number 1 (2017), 626-644.

Algebraic representations of Gaussian Markov combinations

M. Sofia Massa and Eva Riccomagno

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Abstract

Markov combinations for structural meta-analysis problems provide a way of constructing a statistical model that takes into account two or more marginal distributions by imposing conditional independence constraints between the variables that are not jointly observed. This paper considers Gaussian distributions and discusses how the covariance and concentration matrices of the different combinations can be found via matrix operations. In essence, all these Markov combinations correspond to finding a positive definite completion of the covariance matrix over the set of random variables of interest and respecting the constraints imposed by each Markov combination. The paper further shows the potential of investigating the properties of the combinations via algebraic statistics tools. An illustrative application will motivate the importance of solving problems of this type.

Article information

Source
Bernoulli, Volume 23, Number 1 (2017), 626-644.

Dates
Received: November 2013
Revised: August 2015
First available in Project Euclid: 27 September 2016

Permanent link to this document
https://projecteuclid.org/euclid.bj/1475001369

Digital Object Identifier
doi:10.3150/15-BEJ759

Mathematical Reviews number (MathSciNet)
MR3556787

Zentralblatt MATH identifier
1359.62281

Keywords
algebraic statistics conditional independence Gaussian graphical models Markov combinations

Citation

Massa, M. Sofia; Riccomagno, Eva. Algebraic representations of Gaussian Markov combinations. Bernoulli 23 (2017), no. 1, 626--644. doi:10.3150/15-BEJ759. https://projecteuclid.org/euclid.bj/1475001369


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