Outlier Analysis

I am using this for work and its best resource for outlier detection space.

Outstanding book with a diversity of theory and application as well as strengths and weaknesses of techniques. Sure to inspire

Very comprehensive and up-to-date. Statisticians and data scientists should have this on their shelf.

A very detailed and well organized book.

This is an excellent, clearly-written book on outlier analysis and its cousin, anomaly detection. In my opinion the author uses exactly the right amount of equations, coupled with good prose, to clearly convey the concepts and provide a coherent framework to attach them to. If you liked  An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) , I think you will also like this book.Note that although it is well written, this is not an introductory book. To decide if you are ready for it, I recommend downloading and reading the first chapter from the author's website. Look at the exercises at the end; if you can do them easily, you're ready for this book. If you have any difficulty at all, go read or review Hastie and Tibshirani first and then come back.

good

This is a really great book for those with a strong statistics background, but who want a discussion of the concepts in depth but without relying solely on equations. I'm not one to just sit and read a stats book, but I'm reading this one every chance I get because I feel that I am learning how different methods are related conceptually. I think that will pay off in the long term. If you want to get started right away, this isn't the right book because there is no code or discussion of software.

The book was damaged inside and the quality of paper was really rough and cheap, feels like fake book!

Overall although it seems pretty complete I still find this book a mediocre read at best. All in all it is extremely tiresome to read and I did not enjoy reading it. The author should reconsider the way he writes books. Sorry for this harsh critic.Positives:1. The book seems to treat a lot of different concepts and also covers the most important ones2. Considers recent developments in the field3. Is specifically written for people with existing Knowledge in Machine LearningNegatives:1. The whole content of the book could be put into half of its pages2. Concepts (even trivial ones) are explained in unnecessary and complicated sentences3. Explanations lack focus. Instead the author is extremely prolix4. Very repetitive content, too much talk5. Geometrical concepts that could be easily explained in a graphic are instead explained in tiresome written sentences ( the motto “a picture is worth a 1000 words” is ignored)6. Very few rigorous math - instead again - tiresome explanations7. No code examples are provided (okay this critic might be unfair since it is not a coding book - but anyways it would be a nice to have)

This book is well written and detailed enough for an introduction to outlier analysis at under graduate or graduate level.

It's opening up a new area of analysis. Assumes some background in modeling techniques.