Nonparametric Analysis of Univariate Heavy-Tailed Data (PDF)
Research and Practice
(Sprache: Englisch)
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...
multi-component systems such as biometry, economics, ecological
systems, sociology, web access statistics, internet traffic,
biblio-metrics, finance and business. The analysis of...
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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.
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.
Autoren-Porträt von Natalia Markovich
Natalia Markovich - Institute of Control Sciences,Russian Academy of Sciences, Moscow
Having been the Leading Scientist at the Institute of Control
Sciences for the last eleven years, Dr Markovich has had much
experience in this area. An extremely active member of the
statistical community, she has presented many seminars and invited
talks, as well as being involved in numerous international research
projects. She has published over 50 articles and has written
chapters in two books, for Springer-Verlag and Elsevier.
Bibliographische Angaben
- Autor: Natalia Markovich
- 2008, 1. Auflage, 336 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 0470723599
- ISBN-13: 9780470723593
- Erscheinungsdatum: 02.08.2008
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Größe: 4.70 MB
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