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Before using this book, I had done as much research as I could on bootstrap methods using free online resources. I got to the point where these resources were not providing enough detail or theoretical background to allow me to progress any further.
This book covers a lot of topics in bootstrapping where the free online resources were coming up short. The exposition is likely too advanced for someone with little or no mathematics background, but otherwise seems to be at a good level. Without an extensive background in statistics, I have been able to use this book to understand the important ideas and concepts. In most cases I have been able to use the discussion in the book directly to write my own algorithms. In a few cases, it is a little more difficult to jump from the theory described in the book to writing code, but overall it has definitely got me moving in the right direction.
Overall, I'd say this is a very good book and I am happy with the depth and breadth it has added to my study of the field of bootstrapping.
This book is loaded with good text book examples and covers a wide variety of bootstrap applications. It is great as a reference book on the bootstrap or as a course text at a graduate level. Chernick (1999) is a little more up-to-date and covers the classifcation error rate estimation problem that is not addressed in this text. Chernick (1999) also has many more references. Efron and Tibshirani (1993) is another fine text that is a little more intuition based with less mathematics. Fieller's problem with ratio estimation and some other gems are well covered in Efron and Tibshirani but not here. Davison and Hinkley do the best job on time series of any of the bootstrap books with details about moving block bootstrap and some interesting applications.
This book has an excellent ballance between practice and theory. It presents the bootstrap as the powerful tool it is through the ellucidation of practical issues. I strongly recommend this book for everyone interested in improving statistical practice.
This is a well-written book, and I had the basic bootstrap notion figured-out and implemented within a few days, though it will be some time before I develop any serious depth of mastery of the material. For those unfamiliar with the bootstrap method, by a system of resampling from an existing sample of data it provides a means of establishing the standard error for pretty-near any statistical measure (like the standard error of the mean in traditional statistics), as well as the determination of general confidence intervals for those measures, even when the distribution of the data is non-gaussian or unknown. I do find the author's symbolic notation a bit confusing - perhaps TOO compact, and many of the symbolic expressions would really benefit from an associated clearly written paraphrasing (difficult for me to remember all the conventions after putting the book down for a few days). Still, to go from being barely aware of the technique to applying it to the data analysis in a current research project over a period of several weeks suggests that this text does a heck of a good job at conveying the intended introduction.
Books as complicated as this are not written for novices. But some at least try to explain something, some ideas. at least in introduction. This one doesn't try. It is written for those who knows, for a narrow circle. I have a feeling, that it is deliberately written less clearly than possible, to show how smart the author is. Or maybe they don't care.
This book ist excellent for anyone interested in being introduced to bootstrap and doing some self-programming as it covers a wide range of boostrap methods and their applications. However, some examples are some kind far from usual they give a different point of view on some issues.