Ebook: The Analysis of Stochastic Processes using GLIM

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.

---

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.

---

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.
Content:
Front Matter....Pages I-VI
Normal Theory Models and Some Extensions....Pages 1-20
Markov Chains....Pages 21-42
Point and Renewal Processes....Pages 43-78
Survival Curves....Pages 79-102
Growth Curves....Pages 103-113
Time Series: The Time Domain....Pages 114-132
Time Series: The Frequency Domain....Pages 133-154
Repeated Measurements....Pages 155-188
Stochastic Processes and Generalized Linear Models....Pages 189-205
Back Matter....Pages 206-294

---

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.
Content:
Front Matter....Pages I-VI
Normal Theory Models and Some Extensions....Pages 1-20
Markov Chains....Pages 21-42
Point and Renewal Processes....Pages 43-78
Survival Curves....Pages 79-102
Growth Curves....Pages 103-113
Time Series: The Time Domain....Pages 114-132
Time Series: The Frequency Domain....Pages 133-154
Repeated Measurements....Pages 155-188
Stochastic Processes and Generalized Linear Models....Pages 189-205
Back Matter....Pages 206-294
....