The Analysis of Stochastic Processes using GLIM

1. Front Matter

   Pages I-VI

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2. Normal Theory Models and Some Extensions

   • James K. Lindsey

   Pages 1-20

3. Markov Chains

   • James K. Lindsey

   Pages 21-42

4. Point and Renewal Processes

   • James K. Lindsey

   Pages 43-78

5. Survival Curves

   • James K. Lindsey

   Pages 79-102

6. Growth Curves

   • James K. Lindsey

   Pages 103-113

7. Time Series: The Time Domain

   • James K. Lindsey

   Pages 114-132

8. Time Series: The Frequency Domain

   • James K. Lindsey

   Pages 133-154

9. Repeated Measurements

   • James K. Lindsey

   Pages 155-188

10. Stochastic Processes and Generalized Linear Models

    • James K. Lindsey

    Pages 189-205

11. Back Matter

    Pages 206-294

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The aim of this book is to present a survey of the many ways in which the statistical package GLIM may be used to model and analyze stochastic processes. Its emphasis is on using GLIM interactively to apply statistical techniques, and examples are drawn from a wide range of applications including medicine, biology, and the social sciences. It is based on the author's many years of teaching courses along these lines to both undergraduate and graduate students. The author assumes that readers have a reasonably strong background in statistics such as might be gained from undergraduate courses and that they are also familiar with the basic workings of GLIM. Topics covered include: the analysis of survival data, regression and fitting distributions, time series analysis (including both the time and frequency domains), repeated measurements, and generalized linear models.

• Fitting
• Gaussian distribution
• Generalized linear model
• Normal distribution
• Regression analysis
• Time series
• Variance
• Weibull distribution
• analysis of variance
• best fit
• correlation
• linear regression

• Faculté d’Economie, de Gestion et de Sciences Sociales, Université de Liège, Liège, Belgium

  James K. Lindsey

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