Generalized Linear Models With Examples in R 1st edition by Peter Dunn, Gordon Smyth – Ebook PDF Instant Download/Delivery: 1441901175 , 978-1441901170
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Product details:
ISBN 10: 1441901175
ISBN 13: 978-1441901170
Author: Peter Dunn, Gordon Smyth
This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities.
The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text.
Other features include:
• Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals
• Nearly 100 data sets in the companion R package GLMsData
• Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session
Generalized Linear Models With Examples in R 1st Table of contents:
Front Matter
Chapter 1: Statistical Models
Chapter 2: Linear Regression Models
Chapter 3: Linear Regression Models: Diagnostics and Model-Building
Chapter 4: Beyond Linear Regression: The Method of Maximum Likelihood
Chapter 5: Generalized Linear Models: Structure
Chapter 6: Generalized Linear Models: Estimation
Chapter 7: Generalized Linear Models: Inference
Chapter 8: Generalized Linear Models: Diagnostics
Chapter 9: Models for Proportions: Binomial GLMs
Chapter 10: Models for Counts: Poisson and Negative Binomial GLMs
Chapter 11: Positive Continuous Data: Gamma and Inverse Gaussian GLMs
Chapter 12: Tweedie GLMs
Chapter 13: Extra Problems
Back Matter
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Tags: Peter Dunn, Gordon Smyth, Generalized Linear, With Examples