Advanced Statistical Methods for the Analysis of Large Data-Sets

  • Agostino Di Ciaccio
  • Mauro Coli
  • Jose Miguel Angulo Ibanez
Part of the Studies in Theoretical and Applied Statistics book series (STAS)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Clustering Large Data-Sets

    1. Front Matter
      Pages 1-1
    2. Laura Bocci, Isabella Mingo
      Pages 3-12
    3. Marina Marino, Francesco Palumbo, Cristina Tortora
      Pages 13-22
    4. Elvira Romano, Rosanna Verde
      Pages 23-31
    5. Laura M. Sangalli, Piercesare Secchi, Simone Vantini, Valeria Vitelli
      Pages 33-43
  3. Statistics in Medicine

    1. Front Matter
      Pages 45-45
    2. Claudia Angelini, Daniela De Canditiis, Marianna Pensky
      Pages 47-56
    3. Maurice Berk, Cheryl Hemingway, Michael Levin, Giovanni Montana
      Pages 57-67
  4. Integrating Administrative Data

    1. Front Matter
      Pages 79-79
    2. Alessandra Coli, Francesca Tartamella
      Pages 91-99
    3. Fabienne Fortanier, Marjolein Korvorst, Martin Luppes
      Pages 101-111
    4. Paola Vicard, Mauro Scanu
      Pages 113-123
  5. Outliers and Missing Data

About these proceedings

Introduction

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.

Keywords

Clustering for large data-sets Data Mining Data integration and record linkage Large-scale forecasting Managing huge administrative data

Editors and affiliations

  • Agostino Di Ciaccio
    • 1
  • Mauro Coli
    • 2
  • Jose Miguel Angulo Ibanez
    • 3
  1. 1., Dept. of StatisticsUniversity of Roma "La Sapienza"RomaItaly
  2. 2., Dip. di Metodi Quantitativi e Teoria EcoUniversity "G. d'Annunzio" of Chieti-PesPescaraItaly
  3. 3.Fac. Ciencias, Depto. Estadística e Investigación OperaUniversidad de GranadaGranadaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-21037-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-642-21036-5
  • Online ISBN 978-3-642-21037-2
  • About this book