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Advances in Data Analysis

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

  • Book
  • © 2010

Overview

Editors:
  1. Christos H. Skiadas

    1. Data analysis & Forecasting Lab., Technical University of Crete, Chania, Crete, Greece

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  • Real-world applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks
  • New results are emphasized with potential for solving real-world problems
  • For a broad audience of graduate students, researchers, and practitioners in statistics, mathematics, engineering, economics, social science, bioengineering, and bioscience
  • Accessible to graduate students, yet also of interest to experts
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (29 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Data Mining and Text Mining

    1. Front Matter

      Pages 1-1
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  3. Part I Data Mining and Text Mining

    1. Assessing the Stability of Supplementary Elements on Principal Axes Maps Through Bootstrap Resampling. Contribution to Interpretation in Textual Analysis

      • Ramón Alvarez-Esteban, Olga Valencia, Mónica Bécue-Bertaut
      Pages 3-11

    2. A Doubly Projected Analysis for Lexical Tables

      • Simona Balbi, Michelangelo Misuraca
      Pages 13-19

    3. Analysis of a Mixture of Closed and Open-Ended Questions in the Case of a Multilingual Survey

      • Mónica Bécue-Bertaut, Karmele Fernández-Aguirre, Juan I. Modroño-Herrán
      Pages 21-31

    4. Number of Frequent Patterns in Random Databases

      • Loïck Lhote
      Pages 33-45

  4. Information Theory and Statistical Applications

    1. Front Matter

      Pages 47-47
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  5. Part II Information Theory and Statistical Applications

    1. Introduction

      • Koustautiuos Zografos
      Pages 49-50

    2. Measures of Divergence in Model Selection

      • Alex Karagrigoriou, Kyriacos Mattheou
      Pages 51-65

    3. High Leverage Points and Outliers in Generalized Linear Models for Ordinal Data

      • M.C. Pardo
      Pages 67-80

    4. On a Minimization Problem Involving Divergences and Its Applications

      • Athanasios P. Sachlas, Takis Papaioannou
      Pages 81-94

  6. Asymptotic Behaviour of Stochastic Processes and Random Fields

    1. Front Matter

      Pages 95-95
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  7. Part III Asymptotic Behaviour of Stochastic Processes and Random Fields

    1. Remarks on Stochastic Models Under Consideration

      • Ekaterina V. Bulinskaya
      Pages 97-104

    2. New Invariance Principles for Critical Branching Process in Random Environment

      • Valeriy I. Afanasyev
      Pages 105-115

    3. Gaussian Approximation for Multichannel Queueing Systems

      • Larisa G. Afanas’eva
      Pages 117-128

    4. Stochastic Insurance Models, Their Optimality and Stability

      • Ekaterina V. Bulinskaya
      Pages 129-140

    5. Central Limit Theorem for Random Fields and Applications

      • Alexander Bulinski
      Pages 141-150

    6. A Berry – Esseen Type Estimate for Dependent Systems on Transitive Graphs

      • Alexey Shashkin
      Pages 151-156

    7. Critical and Subcritical Branching Symmetric Random Walks on d-Dimensional Lattices

      • Elena Yarovaya
      Pages 157-168

  8. Bioinformatics and Markov Chains

    1. Front Matter

      Pages 169-169
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Keywords

About this book

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, including 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, and 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.

Editors and Affiliations

  • Data analysis & Forecasting Lab., Technical University of Crete, Chania, Crete, Greece

    Christos H. Skiadas

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