A SINful approach to Gaussian graphical model selection
نویسندگان
چکیده
منابع مشابه
A SINful Approach to Gaussian Graphical Model Selection
Multivariate Gaussian graphical models are defined in terms of Markov properties, i.e., conditional independences associated with the underlying graph. Thus, model selection can be performed by testing these conditional independences, which are equivalent to specified zeroes among certain (partial) correlation coefficients. For concentration graphs, covariance graphs, acyclic directed graphs, a...
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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., conditional independences associated with G, which in turn are equivalent to specified zeroes among the set of pairwise partial correlation coefficients. By means of Fisher’s z-transformation and Šidá...
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Multivariate Gaussian graphical models are defined in terms of Markov properties, i.e., conditional independences associated with the underlying graph. Thus, model selection can be performed by testing these conditional independences, which are equivalent to specified zeroes among certain (partial) correlation coefficients. For concentration graphs, covariance graphs, acyclic directed graphs, a...
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Inspired by the success of the Lasso for regression analysis (Tibshirani, 1996), it seems attractive to estimate the graph of a multivariate normal distribution by `1-norm penalised likelihood maximisation. The objective function is convex and the graph estimator can thus be computed efficiently, even for very large graphs. However, we show in this note that the resulting estimator is not consi...
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ژورنال
عنوان ژورنال: Journal of Statistical Planning and Inference
سال: 2008
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2007.05.035