Inference for Heavy-Tailed Data

Liang Peng

ISBN 10: 0128046767 / ISBN 13: 9780128046760

Verlag: Elsevier Science Aug 2017, 2017

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied for many years, which includes recent bias-reduction estimation for tail index and high quantiles with applications in risk management, empirical likelihood based interval estimation for tail index and high quantiles, hypothesis tests for heavy tails, the choice of sample fraction in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are scattered in different statistics journals. Inference for Heavy-Tailed Data Analysis puts these methods into a single place with a clear picture on learning and using these techniques. 180 pp. Englisch.

Bestandsnummer des Verkäufers 9780128046760

Bibliografische Details

• Titel: Inference for Heavy-Tailed Data
• Verlag: Elsevier Science Aug 2017
• Erscheinungsjahr: 2017
• Sprache: Englisch
• ISBN-10: 0128046767
• ISBN-13: 9780128046760
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 228B x 154 H x 11 T Millimeter Breite: 228 mm, Höhe: 154 mm, Tiefe: 11 mm
• Gewicht: 300 Gramm

Über diesen Titel

Inhaltsangabe

Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied for many years, which includes recent bias-reduction estimation for tail index and high quantiles with applications in risk management, empirical likelihood based interval estimation for tail index and high quantiles, hypothesis tests for heavy tails, the choice of sample fraction in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are scattered in different statistics journals. Inference for Heavy-Tailed Data Analysis puts these methods into a single place with a clear picture on learning and using these techniques.

Über die Autorinnen und Autoren

Dr Liang Peng is based at the Department of Risk Management and Insurance at Robinson College of Business, Georgia State University, USA

Dr Yongcheng Qi is based at the Department of Mathematics and Statistics at the University of Minnesota, USA.