Overview
- Unique text devoted to heavy-tails
- The treatment of heavy tails is largely dimensionless
- The text gives attention to both probability modeling and statistical methods for fitting models. Most other books focus on one or the other but not both
- The book emphasizes the broad applicability of heavy-tails to the fields of finance (e.g., value-at- risk), data networks, insurance
- The presentation is clear, efficient and coherent and, balances theory and data analysis to show the applicability and limitations of certain methods
- Several chapters examine in detail the mathematical properties of the methodologies as well as their implementation in the Splus or R statistical languages
- The exposition is driven by numerous examples and exercises
Part of the book series: Springer Series in Operations Research and Financial Engineering (ORFE)
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Table of contents (11 chapters)
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Front Matter
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Introduction
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Crash Courses
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Front Matter
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Statistics
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Front Matter
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More Statistics
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Front Matter
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Appendices
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Front Matter
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Back Matter
Keywords
About this book
This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. Heavy tails are characteristic of phenomena where there is a significant probability of a single huge value impacting system behavior. Record-breaking insurance losses, financial returns, sizes of files stored on a server, transmission rates of files are all examples of heavy-tailed phenomena.
Key features:
Unique text devoted to heavy-tails.
The treatment of heavy tails is largely dimensionless.
The text gives attention to both probability modeling and statistical methods for fitting models. Most other books focus on one or the other but not both.
The book emphasizes the broad applicability of heavy-tails to the fields of finance (e.g., value-at- risk), data networks, insurance.
The presentation is clear, efficient and coherent and, balances theory and data analysis to show the applicability andlimitations of certain methods.
Several chapters examine in detail the mathematical properties of the methodologies as well as their implementation in the Splus or R statistical languages.
The exposition is driven by numerous examples and exercises.
Prerequisites for the reader include a prior course in stochastic processes and probability, some statistical background, some familiarity with time series analysis, and ability to use (or at least to learn) a statistics package such as R or Splus. This work will serve second-year graduate students and researchers in the areas of operations research, statistics, applied mathematics, electrical engineering, financial engineering, networking and economics.
Sidney Resnick is a Professor at Cornell University and has written several well-known bestsellers: A Probability Path (ISBN: 081764055X), Adventures in Stochastic Processes (ISBN: 0817635912) and Extreme Values, Regular Variation, and Point Processes (ISBN: 0387964819).
Authors and Affiliations
Bibliographic Information
Book Title: Heavy-Tail Phenomena
Book Subtitle: Probabilistic and Statistical Modeling
Authors: Sidney I. Resnick
Series Title: Springer Series in Operations Research and Financial Engineering
DOI: https://doi.org/10.1007/978-0-387-45024-7
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag New York 2007
Hardcover ISBN: 978-0-387-24272-9Published: 01 December 2006
Softcover ISBN: 978-1-4419-2024-9Published: 23 November 2010
eBook ISBN: 978-0-387-45024-7Published: 03 December 2007
Series ISSN: 1431-8598
Series E-ISSN: 2197-1773
Edition Number: 1
Number of Pages: XIX, 404
Topics: Probability Theory and Stochastic Processes, Statistical Theory and Methods, Applications of Mathematics, Operations Research, Management Science, Mathematical Modeling and Industrial Mathematics