- Series: Springer Series in Statistics
- Hardcover: 272 pages
- Publisher: Springer; 2nd edition (October 1, 2007)
- Language: English
- ISBN-10: 0387286594
- ISBN-13: 978-0387286594
- Product Dimensions: 6.1 x 0.7 x 9.2 inches
- Shipping Weight: 15.2 ounces (View shipping rates and policies)
- Average Customer Review: 7 customer reviews
- Amazon Best Sellers Rank: #992,634 in Books (See Top 100 in Books)
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An Introduction to Copulas (Springer Series in Statistics) 2nd Edition
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From the reviews of the second edition:
"This introductory and informative text on copulas is clearly written and from an educational standpoint will presented. With more than a hundred examples and over 160 exercise, this book is suitable as a textbook or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics. The second edition maintains the basic organizing of the material and the general level of presentation as the first one from 1999. The major additions are sections on: copula transformation methods; extreme value copulas; copulas with specific analytic or functional properties; tail dependence and quasi-copulas. " (Piotr Jaworski, Zentrablatt MATH, 2009, 1152)
"This introductory and informative text on copulas … is clearly written and from an educational standpoint well presented. … In addition to its primary use as an introductory book on copulas, this text could also serve as a complement to a graduate course or seminar in multivariate analysis focusing on dependence concepts. Readership: people interested in dependence concepts in multivariate analysis. … many will be pleased to have a copy of this new text in their personal library … ." (Radu Theodorescu, Mathematical Reviews, 2006 i)
From the Back Cover
Copulas are functions that join multivariate distribution functions to their one-dimensional margins. The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions.
With 116 examples, 54 figures, and 167 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required. The revised second edition includes new sections on extreme value copulas, tail dependence, and quasi-copulas.
Roger B. Nelsen is Professor of Mathematics at Lewis & Clark College in Portland, Oregon. He is also the author of Proofs Without Words: Exercises in Visual Thinking and Proofs Without Words II: More Exercises in Visual Thinking, published by the Mathematical Association of America.
Top customer reviews
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I do not recommend it for a professional who is learning how to model data correlation at work. This book is too harsh for self study.
I do recommend it for an academic setting. An enthusiastic professor could help teach the concepts, and then assign problems for homework. This would even be appropriate at the undergrad level for a higher level course.
I thoroughly recommend this book, and, if I meet the author, I shall reckon that he owes me a drink.