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Heavy-tailed distributions are typical for phenomena in complex multi-component systems such as biometry, economics, ecological systems, sociology, web access statistics, internet traffic, biblio-metrics, finance and business. The analysis of such distributions requires special methods of estimation due to their specific features. These are not only the slow decay to zero of the tail, but also the violation of Cramer's condition, possible non-existence of some moments, and sparse observations in the tail of the distribution.The book focuses on the methods of statistical analysis of heavy-tailed independent identically distributed random variables by empirical samples of moderate sizes. It provides a detailed survey of classical results and recent developments in the theory of nonparametric estimation of the probability density function, the tail index, the hazard rate and the renewal function.Both asymptotical results, for example convergence rates of the estimates, and results for the samples of moderate sizes supported by Monte-Carlo investigation, are considered. The text is illustrated by the application of the considered methodologies to real data of web traffic measurements.
Frontmatter
Definitions and Rough Detection of Tail Heaviness
Classical Methods of Probability Density Estimation
Heavy-Tailed Density Estimation
Transformations and Heavy-Tailed Density Estimation
Classification and Retransformed Density Estimates
Estimation of High Quantiles
Nonparametric Estimation of the Hazard Rate Function
Nonparametric Estimation of the Renewal Function
List of Main Symbols and Abbreviations
References
Index