Statistical Modelling in GLIM
Published on 1. January 1989
Book
Paperback/Softback
392 pages
978-0-19-852203-4 (ISBN)
Description
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.
It will also provide a text for students meeting the ideas of statistical modelling for the first time.
More details
Language
English
Place of publication
Oxford
United Kingdom
Target group
Professional and scholarly
Illustrations
num. line ill.
numerous line illustrations
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-0-19-852203-4 (9780198522034)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Content
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.