Computational Statistics

Volume 1: Proceedings of the 10th Symposium on Computational Statistics

  • Yadolah Dodge
  • Joe Whittaker

Table of contents

  1. Front Matter
    Pages I-XVI
  2. Prologue

    1. Front Matter
      Pages 1-1
    2. Yadolah Dodge, Joe Whittaker
      Pages 3-7
  3. Statistical Modelling

    1. Front Matter
      Pages 9-9
    2. R. Dennis Cook
      Pages 11-22
    3. Jack J. Dongarra, James W. Demmel, Susan Ostrouchov
      Pages 23-28
    4. Mehmet Sahinoglu, Ibrahim Baltaci, E. H. Spafford
      Pages 47-52
  4. Multivariate Analysis

    1. Front Matter
      Pages 59-59
    2. Robert Cléroux, Jean-Marie Helbling, Normand Ranger
      Pages 71-75
    3. A. de Falguerolles, B. Francis
      Pages 77-82
    4. Pekka J. Korhonen, Aapo Siljamäki
      Pages 83-87
  5. Classification and Discrimination

    1. Front Matter
      Pages 101-101

About these proceedings

Introduction

The Role of the Computer in Statistics David Cox Nuffield College, Oxford OXIINF, U.K. A classification of statistical problems via their computational demands hinges on four components (I) the amount and complexity of the data, (il) the specificity of the objectives of the analysis, (iii) the broad aspects of the approach to analysis, (ill) the conceptual, mathematical and numerical analytic complexity of the methods. Computational requi­ rements may be limiting in (I) and (ill), either through the need for special programming effort, or because of the difficulties of initial data management or because of the load of detailed analysis. The implications of modern computational developments for statistical work can be illustrated in the context of the study of specific probabilistic models, the development of general statistical theory, the design of investigations and the analysis of empirical data. While simulation is usually likely to be the most sensible way of investigating specific complex stochastic models, computerized algebra has an obvious role in the more analyti­ cal work. It seems likely that statistics and applied probability have made insufficient use of developments in numerical analysis associated more with classical applied mathematics, in particular in the solution of large systems of ordinary and partial differential equations, integral equations and integra-differential equations and for the ¢raction of "useful" in­ formation from integral transforms. Increasing emphasis on models incorporating specific subject-matter considerations is one route to bridging the gap between statistical ana.­

Keywords

Statistical Computing calculus classification modeling statistics

Editors and affiliations

  • Yadolah Dodge
    • 1
  • Joe Whittaker
    • 2
  1. 1.Groupe de StatistiqueUniversity of NeuchâtelNeuchâtelSwitzerland
  2. 2.Lancaster UniversityGB-LancasterGreat Britain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-26811-7
  • Copyright Information Physica-Verlag Heidelberg 1992
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-662-26813-1
  • Online ISBN 978-3-662-26811-7
  • About this book