Heavy-Tail Phenomena: Probabilistic and Statistical Modeling (Springer Series in Operations Research and Financial Engineering)

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 many? phenomena where the probability of a single huge value impacts heavily.? Record-breaking insurance losses,? financial-log returns, files sizes stored on a server, transmission rates of files are all examples of? heavy-tailed phenomena.

Key features:

* Unique? text devoted to heavy-tails

* Emphasizes both probability modeling and statistical methods for fitting?models.?? Most? treatments focus on one or the other but not both

* Presents broad applicability? of heavy-tails to the fields of data networks, finance (e.g., value-at- risk), insurance, and hydrology

* Clear, efficient and coherent exposition, balancing? theory and actual data to show the applicability and limitations of certain methods

* Examines in detail the mathematical properties of the methodologies?as well as their implementation in? Splus or R statistical languages

* Exposition 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 applied mathematics, statistics, operations research, electrical engineering, and economics.