Statistical Analysis of Network Data: Methods and Models (Springer Series in Statistics)

Kolaczyk, Eric D. D.:

ISBN 10: 144192776X / ISBN 13: 9781441927767

Verlag: Springer, 2010

Beschreibung

400 pp. Cover with slight signs of wear and discolored at spine, well preserved inside 344 Sprache: Englisch Gewicht in Gramm: 763.

Bestandsnummer des Verkäufers 39769

Bibliografische Details

• Titel: Statistical Analysis of Network Data: Methods and Models (Springer Series in Statistics)
• Verlag: Springer
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 144192776X
• ISBN-13: 9781441927767
• Einband: Paperback
• Auflage: Softcover reprint of hardcover 1st ed.
• Zustand: Gut
• Verkäuferkataloge:
  • Mathematik

Über diesen Titel

Inhaltsangabe

This book is useful both to people in the statistics community and in the various communities in which networks currently are heavily used, such as parts of biology, computer science, engineering, physics, and sociology.

Von der hinteren Coverseite

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 ofinteracting proteins to the characterization of influence of groups of actors in social networks.