Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Datasets
Press/Media
Activities
Fellowships, Honors, and Prizes
Impacts
Search by expertise, name or affiliation
Inference for heavy-tailed data: Applications in insurance and finance
Liang Peng
,
Yongcheng Qi
Mathematics & Statistics
Research output
:
Book/Report
›
Book
14
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Inference for heavy-tailed data: Applications in insurance and finance'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Insurance
100%
Heavy-tailed Data
100%
High Quantile
100%
Tail Index
100%
Social Sciences
33%
Risk Management
33%
Internet Traffic
33%
Hypothesis Testing
33%
Empirical Likelihood
33%
Statistical Inference
33%
Nonlinear Time Series
33%
Data Dependency
33%
Dependent Data
33%
Interval Estimation
33%
Heavy Tails
33%
Bias Reduction
33%
Linear Series
33%
Sampling Fraction
33%
Internet Finance
33%
Quantile Inference
33%
Mathematics
Tail Index
100%
High Quantile
100%
Inferential Statistics
33%
Interval Estimation
33%
Dependent Data
33%
Heavy Tail
33%
Empirical Likelihood
33%
Sample Fraction
33%
Bias Reduction
33%
Linear Time Series
33%
Hypothesis Test
33%
Time Series
33%
Time Series Analysis
33%