Statistical analysis of network data : Methods and models
In the past decade, the study of networks has increased dramatically. Researchers from across the sciences including biology and bioinformatics, computer science, economics, engineering, mathematics, physics, sociology, and statistics are more and more involved with the collection and statistical an...
Tallennettuna:
| Päätekijä: | |
|---|---|
| Aineistotyyppi: | Livre numérique |
| Kieli: | Anglais |
| Julkaistu: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Painos: | 1st ed. 2009. |
| Sarja: | Springer Series in Statistics
|
| Aiheet: | |
| Linkit: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Huomautus: |
Description d'après consultation du 15 novembre 2011 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Statistical Analysis of Network Data, Texte imprimé, 9780387882291 • Statistical analysis of network data, methods and models, Eric D. Kolaczyk, 2009, New York, Springer, 1 volume (XII-386 pages), Springer series in statistics, 978-0-387-88145-4 • Statistical analysis of network data, methods and models, Eric D. Kolaczyk, 2009, New York, Springer, 1 volume (XII-386 pages), Springer series in statistics, 978-0-387-88145-4 |
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| 100 | 1 | |a Kolaczyk, Eric D., |d 1968- | |
| 245 | 1 | 0 | |a Statistical analysis of network data : |b Methods and models |c by Eric D. Kolaczyk. |
| 250 | |a 1st ed. 2009. | ||
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Springer Series in Statistics |x 2197-568X | |
| 500 | |a Description d'après consultation du 15 novembre 2011 | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 504 | |a Bibliogr. Index | ||
| 505 | 1 | |a and Overview Preliminaries Mapping Networks Descriptive Analysis of Network Graph Characteristics Sampling and Estimation in Network Graphs Models for Network Graphs Network Topology Inference Modeling and Prediction for Processes on Network Graphs Analysis of Network Flow Data Graphical Models | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a In the past decade, the study of networks has increased dramatically. Researchers from across the sciences including biology and bioinformatics, computer science, economics, engineering, mathematics, physics, sociology, and statistics are more and more involved with the collection and statistical analysis of network-indexed data. As a result, statistical methods and models are being developed in this area at a furious pace, with contributions coming from a wide spectrum of disciplines. This book provides an up-to-date treatment of the foundations common to the statistical analysis of network data across the disciplines. The material is organized according to a statistical taxonomy, although the presentation entails a conscious balance of concepts versus mathematics. In addition, the examples including extended cases studies are drawn widely from the literature. This book should be of substantial interest both to statisticians and to anyone else working in the area of network science. The coverage of topics in this book is broad, but unfolds in a systematic manner, moving from descriptive (or exploratory) methods, to sampling, to modeling and inference. Specific topics include network mapping, characterization of network structure, network sampling, and the modeling, inference, and prediction of networks, network processes, and network flows. This book is the first such resource to present material on all of these core topics in one place. Eric Kolaczyk is a professor of statistics, and Director of the Program in Statistics, in the Department of Mathematics and Statistics at Boston University, where he also is an affiliated faculty member in the Center for Biodynamics, the Program in Bioinformatics, and the Division of Systems Engineering. His publications on network-based topics include work ranging from the detection of anomalous traffic patterns in computer networks to the prediction of biological function in networks of interacting proteins to the characterization of influence of groups of actors in social networks | ||
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