Confidence intervals and accuracy estimation for heavy-tailed generalized Pareto distributions

Research output: Contribution to journalArticlepeer-review


The generalized Pareto distribution (GPD) is a two-parameter family of distributions which can be used to model exceedances over a threshold. We compare the empirical coverage of some standard bootstrap and likelihood-based confidence intervals for the parameters and upper p-quantiles of the GPD. Simulation results indicate that none of the bootstrap methods give satisfactory intervals for small sample sizes. By applying a general method of D.N. Lawley, correction factors for likelihood ratio statistics of parameters and quantiles of the GPD have been calculated. Simulations show that for small sample sizes accuracy of confidence intervals can be improved by incorporating the computed correction factors to the likelihood-based confidence intervals. While the modified likelihood method has better empirical coverage probability, the mean length of produced intervals are not longer than corresponding bootstrap confidence intervals. This article also investigates the performance of some bootstrap methods for estimation of accuracy measures of maximum likelihood estimators of parameters and quantiles of the GPD.
Original languageEnglish
Pages (from-to)111-123
Publication statusPublished - 2004
Externally publishedYes

Subject classification (UKÄ)

  • Probability Theory and Statistics

Free keywords

  • confidence intervals
  • generalized Pareto distribution
  • maximum likelihood
  • small sample properties
  • Bartlett's correction
  • profile likelihood
  • bootstrap
  • quantiles


Dive into the research topics of 'Confidence intervals and accuracy estimation for heavy-tailed generalized Pareto distributions'. Together they form a unique fingerprint.

Cite this