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Extended Bayesian Information Criteria for Gaussian Graphical Models
TLDR
This paper establishes the consistency of an extended Bayesian information criterion for Gaussian graphical models in a scenario where both the number of variables p and the sample size n grow.
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Discrete chain graph models
The statistical literature discusses different types of Markov properties for chain graphs that lead to four possible classes of chain graph Markov models. The different models are rather well…
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Model selection for Gaussian concentration graphs
A multivariate Gaussian graphical Markov model for an undirected graph G, also called a covariance selection model or concentration graph model, is defined in terms of the Markov properties, i.e.…
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Robust graphical modeling of gene networks using classical and alternative t-distributions
TLDR
It is demonstrated that penalized likelihood inference combined with an application of the EM algorithm provides a computationally efficient approach to model selection in the $t-distribution case.
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Binary models for marginal independence
Summary.  Log‐linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log‐linear models provides a general framework for modelling…
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Lectures on Algebraic Statistics
Markov Bases.- Likelihood Inference.- Conditional Independence.- Hidden Variables.- Bayesian Integrals.- Exercises.- Open Problems.
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PC algorithm for nonparanormal graphical models
TLDR
Analyzing the error propagation from marginal to partial correlations, it is proved that high-dimensional consistency carries over to a broader class of Gaussian copula or nonparanormal models when using rank-based measures of correlation.
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Multiple Testing and Error Control in Gaussian Graphical Model Selection
TLDR
Gaussian graphical model selection can be performed by multiple testing of hypotheses about vanishing (partial) correlation coefficients associated with individual edges that are absent from the graph by demonstrating how this approach allows one to perform model selection while controlling error rates for incorrect edge inclusion.
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Estimation of a covariance matrix with zeros
We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call iterative…
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A SINful approach to Gaussian graphical model selection
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