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Model Selection and Model Averaging (Cambridge Series in Statistical and Probabilistic Mathematics), by Gerda Claeskens, Nils Lid Hjort
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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 frequent 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.
- Sales Rank: #457185 in Books
- Brand: Brand: Cambridge University Press
- Published on: 2008-07-28
- Original language: English
- Number of items: 1
- Dimensions: 9.96" h x .75" w x 6.97" l, 1.90 pounds
- Binding: Hardcover
- 332 pages
- Used Book in Good Condition
Review
"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
About the Author
Gerda Claeskens is Professor in the OR and Business Statistics and Leuven Statistics Research Center at the Catholic University of Leuven, Belgium.
Nils Lid Hjort is Professor of Mathematical Statistics in the Department of Mathematics at the University of Oslo.
Most helpful customer reviews
5 of 5 people found the following review helpful.
I wish I found this book earlier
By Patrick B.
I am so glad I have this book now. It's changed the way that I do statistics. To me reading this book has been like taking the "red pill" in a world where everyone takes the "blue pill" and lives in a 'blissful' world where they don't have to worry about model uncertainty in their research. I know this statement sounds drastic but I truly believe what I am saying. You will fully understand what I mean if you read the book.
I'm a PhD graduate student in economics who does lots of applied statistics research to examine causality. One thing that economists and econometricians RARELY discuss is model uncertainty and pretesting. The common thing to do is to pick a model without much justification, or pretest to select a model. Then they assume that the selected model is the "true" on (the blue pill in action). Now that I know that there are problems with this approach, it's changed the way I see statistical inference for the better.
The section on model averaging is great as well. Model averaging is something that really needs to be picked up by applied statisticians. It has only recently been considered by macroeconomists. This book, and the related literature, have led me to work on my own paper on model averaging in causal inference, where the choice of your model is pretty important. So that's an added bonus.
This book covers model selection and model averaging in depth. The approach is both intuitive and rigorous, so it should appeal to applied statisticians (like me) and more "pure" statisticians. The examples in the book are very eye opening, interesting, and relevant to various research interests. The examples show how poor statistical inference can be when model uncertainty is ignored. Looking at a graph of coverage probabilities for a post AIC pretest estimator kind of blew my mind. It's insane how bad they are.
I highly recommend that all economists who use data get this book. It goes without saying that this book is excellent for statisticians as well.
14 of 19 people found the following review helpful.
this is an excellent book
By Nils Lid Hjort
Well, I'm biased, since I'm the author -- but I do nevertheless think it's excellent. Part of the reason is that it touches topics that I consider "mainstream and important" for the modern statistician.
One needs to be able not only to take any reasonable model (or often enough actually invent a new one, for its specific purpose) and fit it to one's data, complete with interpretation, parameter estimates, and assessment of their precision; but one needs to compare such a model with other candidate models, and somehow converge to a reasonable decision of the type "model seven appears to be better than the other nine I tried out, for these & these specific reasons". The book also pushes the idea that you need to be conscious about the specific use to which the selected model will be put -- a good model for mean structure (often arrived at using the AIC, say) may not do a good job for you if you are more interested in say skewness or extreme aspects; hence the FIC (the Focussed Information Criterion).
This "mainstream and important" aspect has been appreciated when I've been teaching this material, either qua full one-semester course [...] or via say two-day short courses. I have "happy customers" backing up my claim of its usefulness, not only to the theoretically inclined mathematical statisticians (a stratum to which I belong) but also to the so-called practitioners out there.
We've also been complimented for the generous & broad list of real world data examples, ranging from a Nobel Prize quarrel (between Sholokhov and Solzhenitsyn) and life times in Roman Era Egypt to the (almost!) exponential decrease of beer foam and World Cup Football match prediction. We also include a thorough & enlightened discussion of The Quiet Scandal of Statistics.
So, yes, I do hope you buy the book & benefit from using it (and encourage both complimenting and complementing opinions).
Nils Lid Hjort
Professor of mathematical statistics, Department of Mathematics, University of Oslo, Norway
1 of 1 people found the following review helpful.
Highly recommendable book that will improve your understanding of statistics
By Peder Erling Xstbye
This book is highly recommended. For me this book has fulfilled (at least) two purposes. The book has provided me with the necessary analytical tools to apply model selection criteria and model averaging in practice, and to understand what is really going on when doing so. This has certainly substantially improved my abilities in doing statistical inference. Furthermore, the book has provided me with a deeper philosophical understanding of model uncertainty and the possibilities and limitations of statistical inference. If you want to do statistics (at an above basic level) you should simply not take the risk to not read this book. I can also recommend following Nils Lid Hjort's lectures on the topic
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