Abstract

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.

Keywords

MathematicsEstimatorSeries (stratigraphy)StatisticsStability (learning theory)Heavy-tailed distributionBootstrapping (finance)Applied mathematicsProbability distributionEconometricsComputer science

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Publication Info

Year
1999
Type
article
Volume
94
Issue
446
Pages
653-653
Citations
644
Access
Closed

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Russell V. Lenth, Robert J. Alder, Raisa E. Feldman et al. (1999). A Practical Guide to Heavy Tails: Statistical Techniques and Applications. Journal of the American Statistical Association , 94 (446) , 653-653. https://doi.org/10.2307/2670194

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DOI
10.2307/2670194