Description
Book SynopsisLogistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data.
Examples illustrate successful modeling
The text first provides basic terminology and concepts, before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions, the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers
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This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author’s website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect—great clarity.
The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. …
The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author’s goal … .
—Annette J. Dobson, Biometrics, June 2012
Overall this is a comprehensive book, which will provide a very useful resource and handbook for anyone whose work involves modelling binary data.
—David J. Hand, International Statistical Review (2011), 79
… useful as a textbook in a course on logistic regression.
—Andreas Rosenblad, Technometrics, May 2011
Table of ContentsPreface. Introduction. Concepts Related to the Logistic Model. Estimation Methods. Derivation of the Binary Logistic Algorithm. Model Development. Interactions. Analysis of Model Fit. Binomial Logistic Regression. Overdispersion. Ordered Logistic Regression. Multinomial Logistic Regression. Alternative Categorical Response Models. Panel Models. Other Types of Logistic-Based Models. Exact Logistic Regression. Conclusion. Appendices. References. Indices.
