A Practical Guide to Heavy Tails: Statistical Techniques and Applications [Hardcover]

Robert Adler (Editor), Raya Feldman (Editor), Murad Taqqu (Editor)

Book Description

ISBN-10: 0817639519 | ISBN-13: 978-0817639518 | Publication Date: October 26, 1998 | Edition: 1

Aimed at the general practitioner, A Practical Guide to Heavy Tails is a unique collection of essays that is concerned primarily with a large number of techniques and approaches for data analysis. The expository papers, all by distinguished experts, are intended for a wide audience from different disciplines. Thus, the papers run the gamut of applications of heavy-tailed modeling, e.g., telecommunications, the Web, insurance, finance. Along with specific applications are several papers devoted to time series analysis, regression, classical signal/noise detection problems, and the general structure of stable processes, viewed from a modeling standpoint.

Editorial Reviews

Review

"The editors and contributors have done a marvelous job… Professional statisticians and applied mathematicians will find it fascinating for its presentation and documentation of versatile methods for handling troublesome data." —Computing Reviews

Product Details

• Hardcover: 552 pages
• Publisher: Birkhäuser Boston; 1 edition (October 26, 1998)
• Language: English
• ISBN-10: 0817639519
• ISBN-13: 978-0817639518
• Product Dimensions: 10.1 x 7.6 x 1.4 inches
• Shipping Weight: 2.6 pounds (View shipping rates and policies)
• Average Customer Review: 2.5 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #2,062,774 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

17 of 21 people found the following review helpful:

1.0 out of 5 stars Nothing more than a compilation of papers !, January 20, 2001

By 

Steve Uhlig (Berlin, Germany) - See all my reviews
(REAL NAME)   

This review is from: A Practical Guide to Heavy Tails: Statistical Techniques and Applications (Hardcover)

Even if some of the papers whithin the book are certainly important contributions to their field, my impression is that it does not justify paying that price for extended versions of the papers. The book by itself is merely a compilation of the papers but NOTHING MORE ! If you are interested, just get the papers on the web and read them, but don't buy this book !

I hoped i would find some unifying ideas in the book, or even remarks in the extended versions of the papers but nothing... I really wonder how it got published. The only useful information is the extended references for every paper (hence i gave 1 star) ...but it's not worth the price of the book !

I have been extremely disappointed because i'm used to very good work from M. Taqqu, including numerous outstanding papers, but this book is the worst i've ever bought. It does not deserves the name "book".

6 of 9 people found the following review helpful:

4.0 out of 5 stars AN INTERESTING BOOK ON HEAVY-TAILED DISTRIBUTIONS, November 20, 2001

By A Customer

This review is from: A Practical Guide to Heavy Tails: Statistical Techniques and Applications (Hardcover)

In principle, I must confess that I am astonished by the low rating given by the other reviewer! The book is a very good one, emphasizing topics that have not appeared in any other book so far (as, for example, applications of heavy-tailed distributions to financial modeling).

The reason for which I did not give 5 stars is because I am a computer-network specialist, hence I was expecting to see more networking applications than financial ones.

Nevertheless the mathematical content of the book is at a high level; I can assure everyone about it because my first degree is a first-class B.Sc. in Mathematics.

My current job has to do with the design of IP-based multimedia networks and, in fact, I am one of the very few persons (three at most) in the Balkans and probably the only one in Greece who applies the concepts of self-similarity, long-range dependence and heavy-tailed distributions to the design of real communication networks for everyday use. Hence I contend that my opinion matters.

I found particularly helpful the last chapter, which describes numerical techniques. In fact, this is the first book on self-similarity and long-range dependence to address such issues.

I strongly recommend the book for applied mathematicians and financial-modeling experts, while it is quite an interesting reference for network-performance experts. I also recommend it strongly for research students in any of these disciplines.