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This title in other editions

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

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

 

Synopses & Reviews

Publisher Comments:

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 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. 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 (

Synopsis:

This text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models.

Synopsis:

This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models. 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 a statistics package. This work will serve second-year graduate students and researchers in the areas of applied mathematics, statistics, operations research, electrical engineering, and economics.

Table of Contents

Introduction.- Part 1. Crash Courses.- Crash Course I: Regular Variation.- Crash Course II: Weak Convergence; Implications for Heavy Tail Analysis.- Part II. Statistics.- Dipping a Toe in the Statistical Water.- Part III. Probability.- The Poisson Process.- Multivariate Regular Variation and the Poisson Transform.- Weak Convergence and the Poisson Process.- Applied Probability Models and Heavy Tails.- Part IV. More Statistics.-Additional Statistics Topics.- Part V. Appendices.- Notation and Conventions.- Software Routines.- Index.

Product Details

ISBN:
9780387242729
Author:
Resnick, Sidney I.
Publisher:
Springer
Subject:
Finance
Subject:
Linear Programming
Subject:
Applied
Subject:
Mathematical models
Subject:
Probability & Statistics - General
Subject:
Finance -- Mathematical models.
Subject:
Extreme value theory - Mathematical models
Subject:
Statistics
Subject:
Probability Theory and Stochastic Processes
Subject:
Statistical Theory and Methods
Subject:
APPLICATIONS OF MATHEMATICS
Subject:
Operations Research, Management Science
Subject:
Mathematical Modeling and Industrial Mathematics
Subject:
Mathematical Modeling and Industrial Mathematics <P>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
Subject:
Mathematics | Probability and Statistics
Copyright:
Edition Description:
Book
Series:
Springer Series in Operations Research and Financial Engineering
Publication Date:
20061201
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
423
Dimensions:
235 x 178 mm 919 gr

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Related Subjects


Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Computer
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Heavy-Tail Phenomena: Probabilistic and Statistical Modeling (Springer Series in Operations Research and Financial Enginee) New Hardcover
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$67.25 In Stock
Product details 423 pages Springer - English 9780387242729 Reviews:
"Synopsis" by , This text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models.
"Synopsis" by , This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models. 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 a statistics package. This work will serve second-year graduate students and researchers in the areas of applied mathematics, statistics, operations research, electrical engineering, and economics.
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