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Advanced Statistical Methods for the Analysis of Large Data-Sets 2012th Edition
Author(s): Agostino Di Ciaccio (Editor), Mauro Coli (Editor), Jose Miguel Angulo Ibanez (Editor)
- Publisher: Springer
- Publication Date: 16 April 2014
- Edition: 2012th
- Language: English
- Print length: 500 pages
- ISBN-10: 3642443400
- ISBN-13: 9783642443404
Book Description
The theme of the meeting was “Statistical Methods for the Analysis of Large Data-Sets”. In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniques are usually not well suited to managing this kind of data. The conference serves as an important meeting point for European researchers working on this topic and a number of European statistical societies participated in the organization of the event.
The book includes 45 papers from a selection of the 156 papers accepted for presentation and discussed at the conference on “Advanced Statistical Methods for the Analysis of Large Data-sets.”
Editorial Reviews
From the Back Cover
Many research studies in the social and economic fields regard the collection and analysis of large amounts of data. These data sets vary in their nature and complexity, they may be one-off or repeated, they may be hierarchical, spatial or temporal. Examples include textual data, transaction-based data, medical data and financial time-series. Standard statistical techniques are usually not well suited to manage this type of data and many authors have proposed extensions of classical techniques or completely new methods. The huge size of these data-sets and their complexity require new strategies of analysis sometimes subsumed under the terms “data mining” or “predictive analytics”. This volume contains a peer review selection of papers, whose preliminary version was presented at the international meeting of the Italian Statistical Society “Statistical Methods for the analysis of large data-sets”. It collects new ideas, methods and original applications to deal with the complexity and high dimensionality of data.
