Statistical Modelling

1. Front Matter

   Pages I-IX

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2. Papers Presented in the Glim Working Party Session

   1. Introduction to the GLIM session of the GLIM 89 and 4th International Workshop on Statistical Modelling

      • A V Swan

      Pages 1-5

   2. Functional programming for GLMs

      • Michael Clarke, Robert Gilchrist, Anthony Scallan, Mel Slater

      Pages 6-17

   3. GLIM4 - structure and development

      • Mick Green, Brian Francis, Malcolm Bradley

      Pages 18-25

   4. Generalised Linear Models and Some Extensions: Geometry and Algorithms

      • Peter J. Green

      Pages 26-36

   5. The GLIMPSE System

      • Carl M. O’Brien

      Pages 37-43

   6. The Optional Graphics Command Extension to GLIM 3.77

      • R. Allan Reese

      Pages 44-55

3. Invited and Contributed Papers

   1. SABRE: Software for the Analysis of Binary Recurrent Events

      • J. T. Barry, B. J. Francis, R. B. Davies

      Pages 56-63

   2. Score Tests in Overdispersed GLM’s

      • Norman Breslow

      Pages 64-74

   3. A Few Problems with Application of the Kalman Filter

      • Carlo Carraro

      Pages 75-83

   4. Bootstrap Goodness-of-Link Testing in Generalized Linear Models

      • Michael J. Cole, John W. McDonald

      Pages 84-94

   5. Estimation and Tests in a Quasi-likelihood Model with a Non-constant Dispersion Parameter

      • Gauss M. Cordeiro, Clarice G. B. Demétrio

      Pages 95-104

   6. Fitting Non-Exponential Family Nonlinear Models in GLIM by Using the Offset-Facility

      • Gauss M. Cordeiro, Gilberto A. Paula

      Pages 105-114

   7. Graphical Modelling using MIM

      • David Edwards

      Pages 115-126

   8. Beta-Geometric Modelling of Birth Intervals Using GLIM

      • Peter J. Egger, John W. McDonald

      Pages 127-134

   9. Polytomous Logistic Regression: Methods and Application

      • J. Engel

      Pages 135-143

   10. Models for Nested Binary and Ordinal Data

       • Farkad Ezzet, John Whitehead

       Pages 144-150

   11. Extended Kalman Filtering for Nonnormal Longitudinal Data

       • Ludwig Fahrmeir

       Pages 151-156

   12. Modelling Transition Probabilities in the Analysis of Aggregated Data

       • A. Forcina, G. M. Marchetti

       Pages 157-164

   13. Generalisations of the Goodman Association Model for the Analysis of Multi-dimensional Contingency Tables

       • Mick Green

       Pages 165-171

This volume constitutes the Proceedings of the joint meeting of GLIM89 and the 4th International Workshop on statistical Modelling, held in Trento, Italy, from 17 to 21 July 1989. The meeting aimed to bring together researchers interested in the development and application of generalized linear modelling in GLIM and those interested in statistical modelling in its widest sense. This joint meeting built upon the success of previous workshops held in Innsbruck, perugia and Vienna, and upon the two previous GLIM conferences , GLIM82 and GLIM85. The Proceedings of the latter two being available as numbers 14 and 32 in the springer Verlag series of Lecture Notes in Statistics). Much statistical modelling is carried out using GLIM, as is apparent from many of the papers in these Proceedings; however, the Programme Committee were also keen on encouraging papers which discussed more general modelling techniques. Thus about a third of the papers in this volume are outside the GLIM framework. The Programme Committee specifically requested non-theoretical papers in addition to considering theoretical contributions. Thus there are papers in a wide range of practical areas, such as radio spectral occupancy, comparison of birthweights, intervals between births, accidents of railway workers, genetics, demography, medical trials, the social sciences and insurance. A wide range of theoretical developments are discussed, for example, overdispersion, non-exponential family modelling, novel approaches to analysing contingency tables, random effects models, Kalman Filtering, model checking and extensions of Wedderburn's theoretical underpinning of GLMs.

• Fitting
• Generalized linear model
• Likelihood
• algorithms
• best fit
• linear regression

• Istituto di Biometria e Statistica Medica, Milano, Italy

  Adriano Decarli

• Centre for Applied Statistics, Fylde College, Lancaster University, Lancaster, UK

  Brian J. Francis

• Polytechnic of North London, London, UK

  Robert Gilchrist

• Insitut für Statistik, Universität Innsbruck, Innsbruck, Austria

  Gilg U. H. Seeber

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