Advances in Data Analysis: Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks (Statistics for Industry and Technology)

ISBN 10: 0817647988 / ISBN 13: 9780817647988

Verlag: Birkhäuser, 2009

Beschreibung

Bestandsnummer des Verkäufers ABLIING23Feb2416190237798

Bibliografische Details

• Titel: Advances in Data Analysis: Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks (Statistics for Industry and Technology)
• Verlag: Birkhäuser
• Erscheinungsjahr: 2009
• Sprache: Englisch
• ISBN-10: 0817647988
• ISBN-13: 9780817647988
• Einband: Hardcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

This unified volume is a collection of invited chapters presenting recent developments in the field of data analysis, with applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. The book is a useful reference for graduate students, researchers, and practitioners in statistics, mathematics, engineering, economics, social science, bioengineering, and bioscience.

Von der hinteren Coverseite

An outgrowth of the 12^th International Conference on Applied Stochastic Models and Data Analysis, this book is a collection of invited chapters presenting recent developments in the field of data analysis, with applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. Emphasized throughout the volume are new methods with the potential for solving real-world problems in various areas.

The book is divided into eight major sections:

* Data Mining and Text Mining

* Information Theory and Statistical Applications

* Asymptotic Behaviour of Stochastic Processes and Random Fields

* Bioinformatics and Markov Chains

* Life Table Data, Survival Analysis, and Risk in Household Insurance

* Neural Networks and Self-Organizing Maps

* Parametric and Nonparametric Statistics

* Statistical Theory and Methods

Advances in Data Analysis is a useful reference for graduate students, researchers, and practitioners in statistics, mathematics, engineering, economics, social science, bioengineering, and bioscience.