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Advances in Data Analysis

Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks

  • Christos H.  Skiadas

Part of the Statistics for Industry and Technology book series (SIT)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Data Mining and Text Mining

    1. Front Matter
      Pages 1-1
    2. Simona Balbi, Michelangelo Misuraca
      Pages 13-19
    3. Mónica Bécue-Bertaut, Karmele Fernández-Aguirre, Juan I. Modroño-Herrán
      Pages 21-31
  3. Information Theory and Statistical Applications

    1. Front Matter
      Pages 47-47
    2. Koustautiuos Zografos
      Pages 49-50
    3. Alex Karagrigoriou, Kyriacos Mattheou
      Pages 51-65
    4. Athanasios P. Sachlas, Takis Papaioannou
      Pages 81-94
  4. Asymptotic Behaviour of Stochastic Processes and Random Fields

  5. Bioinformatics and Markov Chains

    1. Front Matter
      Pages 169-169
    2. Juliette Martin, Leslie Regad, Anne-Claude Camproux, Grégory Nuel
      Pages 171-180
    3. I. Kipouridis, G. Tsaklidis
      Pages 181-200
  6. Life Table Data, Survival Analysis, and Risk in Household Insurance

    1. Front Matter
      Pages 201-201
    2. Christos H. Skiadas, Charilaos Skiadas
      Pages 203-209
    3. Karl Mosler, Lars Haferkamp
      Pages 211-218
    4. László Márkus, N. Miklós Arató, Vilmos Prokaj
      Pages 219-227
  7. Neural Networks and Self-Organizing Maps

    1. Front Matter
      Pages 229-229
    2. Yiannis S. Boutalis, Theodoros L. Kottas, Manolis A. Christodoulou
      Pages 231-265
    3. Véronique Cariou, Dominique Bertrand
      Pages 267-274
    4. George Atsalakis, Dimitris Nezis, Constantinos Zopounidis
      Pages 275-287
  8. Parametric and Nonparametric Statistics

    1. Front Matter
      Pages 289-289
  9. Statistical Theory and Methods

    1. Front Matter
      Pages 319-319
    2. Aldo Corbellini, Lisa Crosato, Piero Ganugi, Marco Mazzoli
      Pages 321-328
    3. James M. Freeman
      Pages 345-352
  10. Back Matter
    Pages 1-12

About this book

Introduction

An outgrowth of the 12th 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.

Keywords

Fitting Generalized linear model Markov chain Measure Non-parametric statistics Parametric statistics Resampling STATISTICA Sage Survival analysis best fit bioinformatics data analysis databases information theory

Editors and affiliations

  • Christos H.  Skiadas
    • 1
  1. 1.Data analysis & Forecasting Lab.Technical University of CreteChania, CreteGreece

Bibliographic information