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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
Students, practitioners and researchers who need to analyze heavy-tailed data
1. Independent Data: bias-corrected estimators, interval estimation, hypothesis tests, choice of sample fraction
2. Dependent Data: inference for mixing data, ARMA models, GARCH(1,1) models
3. Multivariate Regular Variation: Recent research on hidden regular variation, functional time series.
4. Applications: a tool-box in R will be applied to analyse data sets in insurance and finance
- No. of pages:
- © Academic Press 2018
- 15th August 2017
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Dr Liang Peng is based at the Department of Risk Management and Insurance at Robinson College of Business, Georgia State University, USA
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.
Department of Mathematics and Statistics at the University of Minnesota, USA
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