A Practical Guide to Heavy Tails
Statistical Techniques and Applications
Birkhäuser Verlag GmbH
2nd Edition
Published in July 1998
Book
Hardback
552 pages
978-3-7643-3951-7 (ISBN)
Article exhausted; check for reprint
Description
Presents techniques and approaches for the statistical analysis of heavy-tailed distributions and processes, with a focus on applicability rather than theory. Emphasis is placed upon numerical problems associated with stable distributions, time series analysis and regression.
More details
Edition
2., Nachdr.
Language
English
Place of publication
Basel
Switzerland
Target group
College/higher education
Professional and scholarly
Illustrations
149 schw.-w. Abb.
Dimensions
Height: 26 cm
Width: 18.5 cm
Weight
1134 gr
ISBN-13
978-3-7643-3951-7 (9783764339517)
Schweitzer Classification
Other editions
New editions

Robert Adler | Raya Feldman | Murad Taqqu
A Practical Guide to Heavy Tails
Statistical Techniques and Applications
Book
10/1998
Birkhauser Boston Inc
€213.99
Shipment within 15-20 days
Content
Part 1 Applications: heavy tailed probability distributions in the World Wide Web, M.E. Crovella et al; self-similarity and heavy tails - structural modelling of network traffic, W. Willinger et al; heavy tails in high-frequency financial data, U.A. Muller et al; stable paretian modelling in finance - some empirical and theoretical aspects, S. Mittnik et al; risk management and quantile estimation, F. Bassi et al. Part 2 Time series: analyzing stable time series, R.J. Adler et al; inference for linear processes with stable noise, m. Calder, R.A. Davis; on estimating the intensity of long-range dependence in finite and infinite variance time series, M.S. Taqqu, V. Teverovsky; why non-linearities can ruin the heavy tailed modeller's day, S.I. Resnick; periodogram estimates from heavy-tailed data, T. Mikosch; Bayesian inference for time series with infinite variance stable innovations, N. Ravishanker, Z. Qiou. Part 3 Heavy tail estimation: hill, bootstrap and jackknife estimators for heavy tails, O.V. Pictet et al; characteristic function based estimation of stable distribution parameters, S.M. Kogan. D.B. Williams. Part 4 Regression: bootstrapping signs and permutations for regression with heavy tailed errors - a robust resampling, R. LePage et al; linear regression with stable disturbances, J.H. McCulloch. Part 5 Signal processing: deviation from normality in statistical signal processing - parameter estimation with alpha-stable distributions, P. Tsakalides, C.L. Nikias; statistical modelling and receiver design for multi-user communication networks, G.A. Tsihrintzis. Part 6 Model structures: subexponential distributions, C.M. Goldie, C. Kluppelberg; structure of stationary stable processes, J. Rosinski; tail behaviour of some shot noise processes, G. Samorodnitsky. Part 7 Numerical procedures: numerical approximation of the symmetric stable distribution and density, J.H. McCulloch; table of the maximally-skewed stable distributions, J.H. McCulloch, D.B. Panton; multivariate stable distributions - approximation, estimation, simulation and identification, J.P. Nolan; univariate stable distributions -parametrizations and software, J.P. Nolan.