Inference for Heavy-Tailed Data - 1st Edition - ISBN: 9780128046760, 9780128047507

Inference for Heavy-Tailed Data

1st Edition

Applications in Insurance and Finance

Authors: Liang Peng Yongcheng Qi
eBook ISBN: 9780128047507
Paperback ISBN: 9780128046760
Imprint: Academic Press
Published Date: 15th August 2017
Page Count: 180
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Description

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.

Key Features

  • 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

Readership

Students, practitioners and researchers who need to analyze heavy-tailed data

Table of Contents

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

Details

No. of pages:
180
Language:
English
Copyright:
© Academic Press 2018
Published:
Imprint:
Academic Press
eBook ISBN:
9780128047507
Paperback ISBN:
9780128046760

About the Author

Liang Peng

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

Affiliations and Expertise

Department of Risk Management and Insurance at Robinson College of Business, Georgia State University, USA

Yongcheng Qi

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

Affiliations and Expertise

Department of Mathematics and Statistics at the University of Minnesota, USA