Regression Modeling Strategies |  | Author: Frank E. Jr. Harrell
Publisher: Springer
List Price: $119.00
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Rating:  9 reviews
Sales Rank: 299792
Media: Hardcover
Edition: Corrected
Pages: 600
Number Of Items: 1
Shipping Weight (lbs): 2.5
Dimensions (in): 9.4 x 7.1 x 1.3
ISBN: 0387952322
Dewey Decimal Number: 519.536
EAN: 9780387952321
ASIN: 0387952322
Publication Date: January 10, 2001
Availability: Usually ships in 1-2 business days
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Product Description
There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regressin for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analyzing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modeling.
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Showing reviews 1-5 of 9
 Great practical advice for modelers August 9, 2009
Howard Davidson (Charlotte, NC)
My initial temptation is to say this is the best statistics text ever, but it's all relative. It perfectly suits my current needs and state of development. The book claims to be intended for graduate level students in biostatistics and I think that is a fair assessment (I am self-taught, so how am I to know?).
I haven't even finished yet, but I am reading the text cover-to-cover after first perusing parts of chapter 10. This linear approach is facilitated by Prof. Harrell's excellent writing style.
The text has a practical bent, but with plenty of theory and references to back up the practical advice. You may find Harrell's views to be controversial. I have been forced to reconsider many of my notions about model-building.
I note that "r programming language" is a suggested tag for this product. While Harrell's Design and Hmisc packages are available to R users, the text actually refers to the use of S-PLUS and there may be subtle distinctions. As a Stata user, they're both alien to me, but this hasn't affected my enjoyment of the book.
Exceptionally well-written text July 7, 2009
Daniel Sommerhauser
2 out of 2 found this review helpful
I found "Regression Modeling Strategies" to be a fantastic treatment of a wide assortment of model selection techniques. Harrell's writing style is quite lucid (assuming you've had graduate-level statistics coursework). Model selection/validation is arguably the most critical component of the statistical literature for many industry statisticians, and it is rare to find a textbook solely devoted to the merging of theory with practice. This is not to discredit other applied statistical texts; they represent a necessary foundation to master before a text like Harrell's can be understood with any depth.
It is often said that "All models are wrong but some are useful". To that I would follow with, "In the land of the blind, the one-eyed man is king". Harrell's text will help empower you as a statistical modeler. Personally, I think combining this book with Gelman and Hill's "Data Analysis" text creates about as good of a 1-2 punch that an applied statistician will ever find.
advanced topics in regression with emphasis on model selection January 24, 2008
Michael R. Chernick (Holland PA)
26 out of 26 found this review helpful
Frank Harrell is a Professor who does a lot of consulting in medical research. This book covers a wide variety of topics in regression analysis including many advanced techniques including data reduction, smoothing techniques, variable selection, transformations, shrinkage methods, tree-based methods and resampling. But note the title "Regression Modeling Strategies". Unlike most advanced texts in regression this book emphasizes modeling strategies. So the focus is on things like variable selection and other techniques to avoid overfitting models and diagnostics to look for violations in assumptions such as variance homogeneity or normality and independence of residuals, or stability problems like colinearity.
The book covers an extensive collection of modern techniques for exploratory data analysis. Inferential methods are also considered and he deals appropriately with important issues (particularly for medical research) such as imputation of missing values. Many examples are considered and illustrated in S-PLUS.
Harrell also provides many rules of thumb based on his own experience building models. A lot of the techniques are illustrated using data from the Titanic where it is interesting to see which factors affected the probability of survival. My only disappointment was that there is perhaps too much emphasis on this one particular data set.
A standard regression text would be expected to include linear and nonlinear regression. Harrell goes much deeper including nonparametric regression, logistic regression and survival models (e.g. the Cox proportional hazards model).
 Practical and insightful January 30, 2006
Brant Inman (Somewhere out there)
10 out of 10 found this review helpful
This is a very special statistics book and is unlike any other that I have encountered. Instead of being focused on a specific statistical technique (or family of techniques), Harrell presents a wholistic view of regression modeling for describing real datasets. He starts with the basics of regression assumptions and techniques (splines, shrinkage, etc...), moves on to data management (imputation and reduction), and then addresses the specifics of linear regression, binary logistic regression, ordinal logistic regression, parametric survival regression and Cox regression. Each regression method is approached first with a clear explanation of what the technique is doing and what the critical assumptions are. Then, Harrell demonstrates how to do the analysis in S-Plus/R using a real dataset.
Though I lack the advanced mathematical background necessary to fully explore many statistical textbooks, I did not find this to be a problem for this one. The presentation is that of a teacher: clear with developed reasoning. The production of nomograms was a particularly useful exercise and the S-plus code was also very useful.
I find his opinions on model building strategies to be well though out and persuasive...though I suspect that many may find them controversial. Overall, this is one of the best statistics books that I have purchased.
A great book for anyone who wants to do regression May 31, 2005
Peter Flom (New York City)
6 out of 6 found this review helpful
This is a great book. Although it is not as easy to understand as some other books on regression, I feel that anyone who doesn't understand the ideas put forth here is not really fully competent to do regression analysis. The book is not at fault - statistics is not a simple subject, and regression is not a simple subject, either.
The audience for this book is NOT theoretical statisticians, it is applied statisticians with some background in regression. As a social scientist/statistician, my only complaint is that nearly all the examples are medical - but that's a minor point.
Showing reviews 1-5 of 9
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