GLIM 82: Proceedings of the International Conference on Generalised Linear Models

  • Robert Gilchrist
Conference proceedings

Part of the Lecture Notes in Statistics book series (LNS, volume 14)

Table of contents

  1. Front Matter
    Pages i-vi
  2. J. A. Nelder
    Pages 1-2
  3. R. J. Baker
    Pages 3-24
  4. M. R. B. Clarke
    Pages 25-35
  5. M. Green
    Pages 36-42
  6. M. Slater
    Pages 43-57
  7. P. J. Green
    Pages 69-75
  8. Murray Aitkin
    Pages 76-86
  9. Daryl Pregibon
    Pages 87-97
  10. John Hinde
    Pages 109-121
  11. Anders Ekholm, Juni Palmgren
    Pages 128-143
  12. C. D. Sinclair
    Pages 164-178
  13. Back Matter
    Pages 185-189

About these proceedings


This volume of Lecture Notes in Statistics consists of the published proceedings of the first international conference to be held on the topic of generalised linear models. This conference was held from 13 - 15 September 1982 at the Polytechnic of North London and marked an important stage in the development and expansion of the GLIM system. The range of the new system, tentatively named Prism, is here outlined by Bob Baker. Further sections of the volume are devoted to more detailed descriptions of the new facilities, including information on the two different numerical methods now available. Most of the data analyses in this volume are carried out using the GLIM system but this is, of course, not necessary. There are other ways of analysing generalised linear models and Peter Green here discusses the many attractive features of APL, including its ability to analyse generalised linear models. Later sections of the volume cover other invited and contributed papers on the theory and application of generalised linear models. Included amongst these is a paper by Murray Aitkin, proposing a unified approach to statistical modelling through direct likelihood inference, and a paper by Daryl Pregibon showing how GLIM can be programmed to carry out score tests. A paper by Joe Whittaker extends the recent discussion of the relationship between conditional independence and log-linear models and John Hinde considers the introduction of an independent random variable into a linear model to allow for unexplained variation in Poisson data.


Random variable likelihood modeling statistics

Editors and affiliations

  • Robert Gilchrist
    • 1
  1. 1.Department of MathematicsThe Polytechnic of North LondonHolloway, LondonEngland

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1982
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-90777-2
  • Online ISBN 978-1-4612-5771-4
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site