| Introduction to General and Generalized Linear Models Contributor(s): Madsen, Henrik (Author), Thyregod, Poul (Author) |
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ISBN: 1420091557 ISBN-13: 9781420091557 Publisher: CRC Press OUR PRICE: $118.75 Product Type: Hardcover - Other Formats Published: November 2010 Annotation: Since the mathematics behind generalized linear models is often difficult to follow while the mathematics behind general linear models is well understood, this text describes the methodology behind both models in a parallel setup. After introducing a likelihood framework that is sufficient to cover both approaches, the authors present general linear models, including analysis of covariance, before moving on to more complicated generalized linear models using the same likelihood-based approach. Numerous simulated and real-world examples, implemented using R and SAS, illustrate the methods discussed. The text also provides exercises to further develop understanding. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - General |
| Dewey: 519.535 |
| LCCN: 2010029753 |
| Series: Chapman & Hall/CRC Texts in Statistical Science |
| Physical Information: 0.9" H x 6.2" W x 9.3" (1.25 lbs) 320 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous examples show how the problems are solved with R. After describing the necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel results for the general linear models first, since they are easier to understand and often more well known. The authors then explore random effects and mixed effects in a Gaussian context. They also introduce non-Gaussian hierarchical models that are members of the exponential family of distributions. Each chapter contains examples and guidelines for solving the problems via R. Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to general and generalized linear models using the popular and powerful likelihood techniques. Ancillary materials are available at www.imm.dtu.dk/ hm/GLM |