Titlebook: Statistical Analysis of Graph Structures in Random Variable Networks; V. A. Kalyagin,A. P. Koldanov,P. M. Pardalos Book 2020 The Author(s)

Enrage

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Pigeon

Introduction,random variables and edges reflect some kind of similarity among them. Statistical analysis of this type of networks involves uncertainty. This aspect is not well covered in existing literature. The main goal of the book is to develop a general approach to handle uncertainty of network structure ide

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Random Variable Networks,model, we give definitions of specific network structures: maximum spanning tree, planar maximally filtered graph, concentration graph, threshold graph, maximum clique, and maximum independent set in the threshold graph. All definitions are illustrated by examples. Then we consider a large class of

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Network Structure Identification Algorithms,ication. We show that this problem can be considered as multiple testing problem and describe different multiple testing algorithms for threshold graph identification. We note that existing practice of market graph identification can be considered in the proposed framework. In the same way, we discu

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Robustness of Network Structure Identification,bility of the risk function with respect to the distribution of the vector . from some class of distributions (distribution free property). We show that popular identification algorithms based on sample Pearson correlations are not robust in the class of elliptical distributions. To overcome this is

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