Statistical modelling in GLIM
著者
書誌事項
Statistical modelling in GLIM
（Oxford statistical science series, 4）
Clarendon Press , Oxford University Press, 1989
- : hard
- : pbk
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注記
Bibliography: p. [316]-320
Includes indexes
内容説明・目次
- 巻冊次
-
: pbk ISBN 9780198522034
内容説明
The analysis of data by statistical modelling is becoming increasingly important. This book presents both the theory of statistical modelling with generalized linear models and the application of the theory to practical problems using the widely available package GLIM. The authors have taken pains to integrate the theory with many practical examples which illustrate the value of interactive statistical modelling. Throughout the book theoretical issues of formulating and simplifying models are discussed, as are problems of validating the models by the detection of outliers and influential observations. The book arises from short courses given at the University of Lancaster's Centre for Applied Statistics, with an emphasis on practical programming in GLIM and numerous examples. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential and Weibull distributions. A feature of the book is a detailed discussion of survival analysis. Statisticians working in a wide range of fields, including biomedical and social sciences, will find this book an invaluable desktop companion to aid their statistical modelling.
It will also provide a text for students meeting the ideas of statistical modelling for the first time.
目次
- Part 1 Introducing GLIM 3: getting started in GLIM 3. Part 2 Statistical modelling and statistical inference: the Bernoulli distribution for binary data
- types of variables
- definition of a statistical model
- model criticism
- likelihood-based confidence intervals. Part 3 Normal regression and analysis of variance: the normal distribution and the Box-Cox transformation family
- link functions and transformations
- regression models for prediction
- the use of regression models for calibration
- fatorial designs
- midding data. Part 4 Binomial response data: binary responses
- transformations and link functions
- contingency table construction from binary data
- multidimensional contingency tables with a binary response. Part 5: multinomial and Poisson response data. Part 6 Survival data: probability plotting with censored data - the Kaplan-Meier estimator
- the Weibull distribution
- the Cox proportional hazards model and the piecewise exponential distribution
- the logistic and log logistic distributions
- time-dependent explanatory variables. Appendices: discussion
- GLIM directives
- system defined structures in GLIM
- datasets and macros.
- 巻冊次
-
: hard ISBN 9780198522041
内容説明
An introduction to both the theory of statistical models and the practical implementation of these techniques in the analysis of data using the package GLIM 3, the statistical package for Generalized Linear Interactive Modelling, developed by the Working Party on Statistical Computing of the Royal Statistical Society. The authors have aimed to integrate both the theoretical and practical aspects, thus all the statistical principles which are discussed are illustrated by worked examples using GLIM's interactive facilities. A full description of the use of GLIM 3 for model fitting is given with detailed discussions of many examples. This book was written from 1982-1987 as part of an Economic and Social Research Council research programme at the Centre for Applied Statistics in the analysis of complex social data, which supported Dorothy Anderson and John Hinde. There are several way this book can be used. It is written in sequence which is intended to be appropriate for intensive courses. Chapter 1 gives a gentle introduction to GLIM 3 for novices and chapter 2 a general introduction to the principles of statistical modelling, with two simple examples.
This chapter also develops the necessary theory of maximum likelihood estimation and likelihood ratio testing. Chapter 3 discusses the normal model, chapter 4 binomial data, chapter 5 multinomial and Poisson data and chapter 6 survival data. This work should be of value to statisticians working in a wide range of fields including biomedical research and the social sciences as well as providing a "hands on" guide for students in these areas using these techniques for the first time.
目次
- Part 1 Introducing GLIM 3: getting started in GLIM 3. Part 2 Statistical modelling and statistical inference: the Bernoulli distribution for binary data
- types of variables
- definition of a statistical model
- model criticism
- likelihood-based confidence intervals. Part 3 Normal regression and analysis of variance: the normal distribution and the Box-Cox transformation family
- link functions and transformations
- regression models for prediction
- the use of regression models for calibration
- fatorial designs
- midding data. Part 4 Binomial response data: binary responses
- transformations and link functions
- contingency table construction from binary data
- multidimensional contingency tables with a binary response. Part 5: multinomial and Poisson response data. Part 6 Survival data: probability plotting with censored data - the Kaplan-Meier estimator
- the Weibull distribution
- the Cox proportional hazards model and the piecewise exponential distribution
- the logistic and log logistic distributions
- time-dependent explanatory variables. Appendices: discussion
- GLIM directives
- system defined structures in GLIM
- datasets and macros.
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