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
    2. Antonio Ciampi, Lisa Hendricks, Zhiyi Lou
      Pages 131-136
    3. J. F. Durand
      Pages 145-149
    4. O. J. W. F. Kardaun, J. W. P. F. Kardaun, S.-I. Itoh, K. Itoh
      Pages 163-170
    5. Francesco Mola, Roberta Siciliano
      Pages 179-184
  6. Symbolic and Relational Data

    1. Front Matter
      Pages 191-191
    2. F. A. T. de Carvalho, E. Diday
      Pages 221-226
    3. C. Chabanon, Y. Lechevallier, S. Millemann
      Pages 227-232
  7. Graphical Models

    1. Front Matter
      Pages 233-233
    2. Nanny Wermuth, D. R. Cox
      Pages 235-249
    3. J. H. Badsberg
      Pages 251-256
    4. M. J. Brewer, C. G. G. Aitken, Z. Luo, A. Gammerman
      Pages 257-262
    5. F. M. Malvestuto
      Pages 263-267
    6. George Ostrouchov
      Pages 269-274
  8. Time Series Models

    1. Front Matter
      Pages 281-281
    2. Victor Gómez, Agustín Maravall, Daniel Peña
      Pages 283-296
    3. W. Alem, M. Karasek, W. Iskander
      Pages 309-314

About these proceedings


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.­


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