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What would be a good book for non-parametric statistics. Not just the introduction but advanced level. Also I am looking at something I can use for learn and not for reference.

In particular I am looking for a book that can contain basics behind non-parameteric methods, non-parameter inference, methods to evaluate non parametrics, e.g., KS test, $t$ test, etc. , bootstrapping ....

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    $\begingroup$ Nonparametric statistics is a large field, but I'd guess All of Nonparametric Statistics by Larry Wassermann should be a reasonable starting point. I don't know if I would call it "advanced level" but he sketches the proofs of many of the results in the book. Topics include the bootstrap, smoothing techniques, density estimation, regression, and lots of other things. No nonparametric Bayes, however. $\endgroup$ – guy Mar 4 '13 at 21:41
  • $\begingroup$ I have checked that one out but its more like a reference book than learning material. No? $\endgroup$ – I J Mar 4 '13 at 22:42
  • $\begingroup$ I disagree, it should be fine for learning from. If I'm remembering correctly, he wrote it for people who hadn't seen nonparametric methods before, such computer science students he teaches. $\endgroup$ – guy Mar 4 '13 at 23:18
  • $\begingroup$ Are you looking for 'distribution free' stuff, or nonparametrics in the sense of "infinite parametric", whether it applies to distributions, relationships between variables or whatever else? For example, I can assume a linear relationship for $(Y|X=x)$ without a distributional assumption, or I can assume that $Y$ is normal and not assume the relationship with $X$ is anything but 'smooth'... both can be referred to as 'nonparametric', even though each is parametric in one aspect (if potentially infinite-parametric in another). $\endgroup$ – Glen_b -Reinstate Monica Mar 5 '13 at 0:50
  • $\begingroup$ I am looking for distribution free stuff . $\endgroup$ – I J Mar 5 '13 at 3:02
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I think of the Encyclopaedia Britannica of nonparametric statistics as being:

I'm not sure if I would characterize this as introductory or advanced. Many of the sections are a bit terse, in my opinion, and are written with a good deal of mathematical notation. This will be intimidating / off-putting for people who have some math anxiety. On the other hand, it's not really deriving theorems, it's just using mathematical notation to express the ideas. There are some problems included at the end of each section; you could definitely use the book to learn nonparametric statistics.

For a treatment that is much more introductory:

will be much less intimidating, I think. I have skimmed some portions of it, and it seems to be a gentle introduction for people who don't have a strong statistical background. It is very clear, but does not have anything like the depth or coverage of Hollander & Wolfe.

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  • $\begingroup$ I don't have a recommendation for a book that focuses primarily on the mathematical theory behind nonparametric statistics. $\endgroup$ – gung - Reinstate Monica Apr 20 '13 at 3:57
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    $\begingroup$ In november 2013 the third edition of Hollander & Wolfe appeared and a corresponding GNU R package NSM3 is available. $\endgroup$ – Marcus Ritt Jun 26 '14 at 2:18
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You may want to check out this book. The title says it all ;) The reviews for the book are outstanding. I cannot tell for myself, since I have not read it yet. But it seems to have all the theoretical background you are looking for, plus it has also a focus on applications, i.e. it well help you to put those techniques into use quickly.

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    $\begingroup$ Provide the name of book in the answer, not only the link, please! :) $\endgroup$ – Ronie Mar 13 '18 at 1:38

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