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Maximum Likelihood Estimation in Gaussian AMP Chain Graph Models and Gaussian Ancestral Graph Models
, 2004
"... The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with datageneration by natural bloc ..."
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The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with datageneration by natural blockrecursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm. Key words: AMP chain graph, graphical model, iterative partial maximization, multivariate normal distribution, maximum likelihood estimation 1
Multiple testing and error control in Gaussian graphical model selection
 Statistical Science
"... Abstract. Graphical models provide a framework for exploration of multivariate dependence patterns. The connection between graph and statistical model is made by identifying the vertices of the graph with the observed variables and translating the pattern of edges in the graph into a pattern of cond ..."
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Cited by 12 (2 self)
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Abstract. Graphical models provide a framework for exploration of multivariate dependence patterns. The connection between graph and statistical model is made by identifying the vertices of the graph with the observed variables and translating the pattern of edges in the graph into a pattern of conditional independences that is imposed on the variables ’ joint distribution. Focusing on Gaussian models, we review classical graphical models. For these models the defining conditional independences are equivalent to vanishing of certain (partial) correlation coefficients associated with individual edges that are absent from the graph. Hence, Gaussian graphical model selection can be performed by multiple testing of hypotheses about vanishing (partial) correlation coefficients. We show and exemplify how this approach allows one to perform model selection while controlling error rates for incorrect edge inclusion. Key words and phrases: Acyclic directed graph, Bayesian network, bidirected graph, chain graph, concentration graph, covariance graph, DAG, graphical model, multiple testing, undirected graph. 1.
A Graphical Representation of Equivalence Classes of AMP Chain Graphs
, 2005
"... This paper deals with chain graph models under alternative AMP interpretation. A new representative of an AMP Markov equivalence class, called the largest deflagged graph, is proposed. The representative is based on revealed internal structure of the AMP Markov equivalence class. More specifically, ..."
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This paper deals with chain graph models under alternative AMP interpretation. A new representative of an AMP Markov equivalence class, called the largest deflagged graph, is proposed. The representative is based on revealed internal structure of the AMP Markov equivalence class. More specifically, the AMP Markov equivalence class decomposes into finer strong equivalence classes and there exists a distinguished strong equivalence class among those forming the AMP Markov equivalence class. The largest deflagged graph is the largest chain graph in that distinguished strong equivalence class. A composed graphical procedure to get the largest deflagged graph on the basis of any AMP Markov equivalent chain graph is presented. In general, the largest deflagged graph differs from the AMP essential graph, which is another representative of the AMP Markov equivalence class.
Sequences of regressions and their independences
, 2012
"... Ordered sequences of univariate or multivariate regressions provide statistical modelsfor analysingdata fromrandomized, possiblysequential interventions, from cohort or multiwave panel studies, but also from crosssectional or retrospective studies. Conditional independences are captured by what we ..."
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Cited by 4 (1 self)
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Ordered sequences of univariate or multivariate regressions provide statistical modelsfor analysingdata fromrandomized, possiblysequential interventions, from cohort or multiwave panel studies, but also from crosssectional or retrospective studies. Conditional independences are captured by what we name regression graphs, provided the generated distribution shares some properties with a joint Gaussian distribution. Regression graphs extend purely directed, acyclic graphs by two types of undirected graph, one type for components of joint responses and the other for components of the context vector variable. We review the special features and the history of regression graphs, prove criteria for Markov equivalence anddiscussthenotion of simpler statistical covering models. Knowledgeof Markov equivalence provides alternative interpretations of a given sequence of regressions, is essential for machine learning strategies and permits to use the simple graphical criteria of regression graphs on graphs for which the corresponding criteria are in general more complex. Under the known conditions that a Markov equivalent directed acyclic graph exists for any given regression graph, we give a polynomial time algorithm to find one such graph.
LEARNING AMP CHAIN GRAPHS AND SOME MARGINAL MODELS THEREOF UNDER FAITHFULNESS: ADDENDUM
"... A distinguished member of a class of triplex equivalent AMP CGs is the socalled essential graph G ∗ (Andersson and Perlman, 2006): An edge A → B is in G ∗ if and only if A ← B is in no member of the class. Unfortunately, our learning algorithm in Table 1 in the main text does not output an essentia ..."
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A distinguished member of a class of triplex equivalent AMP CGs is the socalled essential graph G ∗ (Andersson and Perlman, 2006): An edge A → B is in G ∗ if and only if A ← B is in no member of the class. Unfortunately, our learning algorithm in Table 1 in the main text does not output an essential graph, as we have shown in Section 3 with an example. However, it can easily be modified for this task, as we show below. It is worth mentioning that an algorithm for this task has been proposed before (Andersson and Perlman, 2004, Section 7). However, as far as we know, its correctness has not been proven. We do prove the correctness of our algorithm. Table 1. Algorithm for constructing the essential graph in a class of triplex equivalent CGs. Input: A CG G. Output: The essential graph G ∗ in the class of triplex equivalent CGs containing G. 1 For each ordered pair of nonadjacent nodes A and B in G
Spatial Graphical Models with Discrete and Continuous Components
, 2012
"... Abstract approved: ..."
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva106668 LEARNING AMP CHAIN GRAPHS AND SOME MARGINAL MODELS
"... Learning AMP chain graphs and some marginal models thereof under faithfulness ..."
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Learning AMP chain graphs and some marginal models thereof under faithfulness