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Product details
File Size: 14731 KB
Print Length: 582 pages
Publisher: Springer; 2 edition (August 14, 2015)
Publication Date: August 14, 2015
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B0140XQAXI
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Amazon Best Sellers Rank:
#397,782 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
I bought this text after using and learning about Professor Harrell's contributions through the literature and through the R and S computing communities. Others have written how wonderful Professor Harrell's software is to use, and how carefully he has thought about the entire cycle of doing statistics, including transparent analyses and publication using LaTeX.But the book is a joy, balancing theoretical concerns, a masterful selection of literature, and solid assessments and presentations of actual examples. Professor Harrell demonstrates how powerful these techniques are, and the material moving them. These insights are key in a time when increasingly people want to just dump numbers into a package and get something out of it, an analytical behavior often justified by cost and time pressures. (If it's wrong results you want, it's trivially easy to get those, even if they LOOK wrong.) Professor Harrell shows it needn't be that hard, with tools from the CRAN (the R community source), many good ones which he and colleagues contributed. But he also shows that it's important to keep an eye on the parts of these, and be wary of pitfalls.Professor Harrell is also candid in his assessments, even after giving enthusiasts for a technique he critiques the benefit of the doubt. I find his comparison of cross-validation with bootstrap validation wonderful, and his discussion of standard assessments of models like R^2 refreshing.Check out his lectures, too:[...][...][...]
Great book. Wish it had more coverage of more GLM analysis (Gamma and Poisson regression.) Excellent and insightful R-code.
Informed, experienced update of this classical work on statistics, bringing to it additional years of Professor Frank Harrell, Jr's in-depth experience, rules of thumb, and practicals.
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.
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.
Frank Harrell's widely-cited "Regression Modeling Strategies" was far ahead of its time and remains very relevant today. The book is a wakeup call that many long-established data analytic practices are problematic.
I'm a graduate student in the life sciences and was looking for a book on multiple logistic regressions. My advisor suggest this book and I have not been disappointed. Harrell does a good job of balancing theory with application throughout the book. The inclusion of S-Plus/R code was also beneficial. Furthermore, I was impressed that the code still worked despite the booking being over a decade old!
If you want to move past the "just use cross validation" stage of your ML work and improve your model's generalization (and understand why and when to use techniques like bootstrapping) this is the book for you.
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