Introduction to Statistical Modelling

ISBN-10: 1584889500

ISBN-13: 9781584889502

Edition: 3rd 2008 (Revised)

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Description:

Popular for its accessible, concise, and clear introduction to this key statistical methodology, An Introduction to Generalized Linear Models: Third Edition provides a wealth of examples from such diverse fields as business, medicine, engineering, and the social sciences. Emphasizing graphical methods for exploratory data analysis and visualization, this new edition offers more material on Bayesian methodology and additional advice on implementing methods using statistical software. It also has updated the examples and exercises, enhancing its suitability for self-study. The text now includes an appendix of selected solutions along with a full solutions manual available to lecturers.
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Book details

List price: $74.95
Edition: 3rd
Copyright year: 2008
Publisher: CRC Press LLC
Publication date: 5/12/2008
Binding: Hardcover
Pages: 320
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 1.232
Language: English

Preface
Introduction
Background
Scope
Notation
Distributions related to the Normal distribution
Quadratic forms
Estimation
Exercises
Model Fitting
Introduction
Examples
Some principles of statistical modelling
Notation and coding for explanatory variables
Exercises
Exponential Family and Generalized Linear Models
Introduction
Exponential family of distributions
Properties of distributions in the exponential family
Generalized linear models
Examples
Exercises
Estimation
Introduction
Example: Failure times for pressure vessels
Maximum likelihood estimation
Poisson regression example
Exercises
Inference
Introduction
Sampling distribution for score statistics
Taylor series approximations
Sampling distribution for MLEs
Log-likelihood ratio statistic
Sampling distribution for the deviance
Hypothesis testing
Exercises
Normal Linear Models
Introduction
Basic results
Multiple linear regression
Analysis of variance
Analysis of covariance
General linear models
Exercises
Binary Variables and Logistic Regression
Probability distributions
Generalized linear models
Dose response models
General logistic regression model
Goodness of fit statistics
Residuals
Other diagnostics
Example: Senility and WAIS
Exercises
Nominal and Ordinal Logistic Regression
Introduction
Multinomial distribution
Nominal logistic regression
Ordinal logistic regression
General comments
Exercises
Poisson Regression and Log-Linear Models
Introduction
Poisson regression
Examples of contingency tables
Probability models for contingency tables
Log-linear models
Inference for log-linear models
Numerical examples
Remarks
Exercises
Survival Analysis
Introduction
Survivor functions and hazard functions
Empirical survivor function
Estimation
Inference
Model checking
Example: Remission times
Exercises
Clustered and Longitudinal Data
Introduction
Example: Recovery from stroke
Repeated measures models for Normal data
Repeated measures models for non-Normal data
Multilevel models
Stroke example continued
Comments
Exercises
Bayesian Analysis
Frequentist and Bayesian paradigms
Priors
Distributions and hierarchies in Bayesian analysis
WinBUGS software for Bayesian analysis
Exercises
Markov Chain Monte Carlo Methods
Why standard inference fails
Monte Carlo integration
Markov chains
Bayesian inference
Diagnostics of chain convergence
Bayesian model fit: the DIC
Exercises
Example Bayesian Analyses
Introduction
Binary variables and logistic regression
Nominal logistic regression
Latent variable model
Survival analysis
Random effects
Longitudinal data analysis
Some practical tips for WinBUGS
Exercises
Appendix
Software
References
Index
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