Inference for Heavy-Tailed Data

Applications in Insurance and Finance

Nonfiction, Science & Nature, Mathematics, Applied
Cover of the book Inference for Heavy-Tailed Data by Liang Peng, Yongcheng Qi, Elsevier Science
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Liang Peng, Yongcheng Qi ISBN: 9780128047507
Publisher: Elsevier Science Publication: August 11, 2017
Imprint: Academic Press Language: English
Author: Liang Peng, Yongcheng Qi
ISBN: 9780128047507
Publisher: Elsevier Science
Publication: August 11, 2017
Imprint: Academic Press
Language: English

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.

  • Contains comprehensive coverage of new techniques of heavy tailed data analysis
  • Provides examples of heavy tailed data and its uses
  • Brings together, in a single place, a clear picture on learning and using these techniques
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Elsevier Science

Cover of the book Writing for Interaction by Liang Peng, Yongcheng Qi
Cover of the book The Exposome by Liang Peng, Yongcheng Qi
Cover of the book Instrumental Assessment of Food Sensory Quality by Liang Peng, Yongcheng Qi
Cover of the book Advances in Quantum Chemistry by Liang Peng, Yongcheng Qi
Cover of the book The Compliance Response to Misconduct Allegations by Liang Peng, Yongcheng Qi
Cover of the book Sustainable Communities Design Handbook by Liang Peng, Yongcheng Qi
Cover of the book Database Design: Know It All by Liang Peng, Yongcheng Qi
Cover of the book Advances in the Study of Behavior by Liang Peng, Yongcheng Qi
Cover of the book Pile Design and Construction Rules of Thumb by Liang Peng, Yongcheng Qi
Cover of the book Materials by Liang Peng, Yongcheng Qi
Cover of the book Innovative Neuromodulation by Liang Peng, Yongcheng Qi
Cover of the book Monitoring and Evaluation of Biomaterials and their Performance In Vivo by Liang Peng, Yongcheng Qi
Cover of the book Control of Complex Systems by Liang Peng, Yongcheng Qi
Cover of the book Failure Analysis Case Studies II by Liang Peng, Yongcheng Qi
Cover of the book How to Defeat Advanced Malware by Liang Peng, Yongcheng Qi
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy