Model Selection and Model Averaging
Model Selection and Model Averaging
CAD$113.95
Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed Hamlet actually a good writer? Choosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled, with discussions of frequentist and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R code.
- Not just oriented towards one single model choice criterion, but to many, including the AIC, BIC, DIC and FIC
- Exercises and literature reviews at the end of each chapter make this ideal for students
- All data analyses compatible with open-source R software; data sets and R code available from companion Website
Reviews & endorsements
"All data analyses are compatible with open-source R software, and data sets and R code are available from a companion web site."
Book News
"Overall, given the inviting style of the presentation and the quality of the material, this book could be quite a catch for graduate students as well as for practitioners where models really do make a difference."
Ita Cirovic Donev, MAA Reviews
"'This is a good textbook for a master-level statistical course about model selection.' It covers many important concepts and methods about model selection."
Mathematical Reviews
"This book is comprehensive in its treatment of the subject and will probably teach something new, even to the most experienced researchers in model selection. The authors have succeeded in bringing together a coherent volume, which gives a state of the art account of the current practice in model selection and comparison, containing a plethora of asymptotic (sometimes new) results, which can be used to compare different model choice criteria. Most importantly, this is the sole volume dedicated to this subject, taking a fully statistical as opposed to an information theoretic approach to the topic of model selection. This book will be attractive to a wide range of graduate students and researchers, users or developers of model choice criteria, of all statistical persuasions."
Cedric E. Ginestet, Statistics in Society
"This book is the best available review of model selection from a statistical standpoint. It has a very nice combination of just-enough statistical theory with lots of non-trivial worked examples, and the theory is well-presented and useful, without much being left to folklore."
Cosma Shalizi, The Bactra Review
Product details
September 2008Adobe eBook Reader
9780511421235
0 pages
0kg
46 b/w illus. 35 tables 65 exercises
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Preface
- A guide to notation
- 1. Model selection: data examples and introduction
- 2. Akaike's information criterion
- 3. The Bayesian information criterion
- 4. A comparison of some selection methods
- 5. Bigger is not always better
- 6. The focussed information criterion
- 7. Frequentist and Bayesian model averaging
- 8. Lack-of-fit and goodness-of-fit tests
- 9. Model selection and averaging schemes in action
- 10. Further topics
- Overview of data examples
- Bibliography
- Author index
- Subject index.
