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Evolutionary Statistical Procedures

An Evolutionary Computation Approach to Statistical Procedures Designs and Applications

  • Roberto Baragona
  • Francesco Battaglia
  • Irene Poli

Part of the Statistics and Computing book series (SCO)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Roberto Baragona, Francesco Battaglia, Irene Poli
    Pages 1-4
  3. Roberto Baragona, Francesco Battaglia, Irene Poli
    Pages 5-61
  4. Roberto Baragona, Francesco Battaglia, Irene Poli
    Pages 63-84
  5. Roberto Baragona, Francesco Battaglia, Irene Poli
    Pages 85-124
  6. Roberto Baragona, Francesco Battaglia, Irene Poli
    Pages 125-157
  7. Roberto Baragona, Francesco Battaglia, Irene Poli
    Pages 159-197
  8. Roberto Baragona, Francesco Battaglia, Irene Poli
    Pages 199-260
  9. Back Matter
    Pages 261-276

About this book

Introduction

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

Keywords

Data Analysis Design of Experiments Evolutionary Computation Metaheuristics Optimization Algorithms

Authors and affiliations

  • Roberto Baragona
    • 1
  • Francesco Battaglia
    • 2
  • Irene Poli
    • 3
  1. 1., Department ofSapienza University of RomeRomeItaly
  2. 2., StatisticsSapienza University of RomeRomaItaly
  3. 3., StatisticsCa' Foscari University of VeniceVeniceItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-16218-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-642-16217-6
  • Online ISBN 978-3-642-16218-3
  • Series Print ISSN 1431-8784
  • Buy this book on publisher's site