Strongly-consistent, distribution-free confidence intervals for quantiles

David Gilat*, T. P. Hill

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Strongly-consistent, distribution-free confidence intervals are derived to estimate the fixed quantiles of an arbitrary unknown distribution, based on order statistics of an iid sequence from that distribution. This new method, unlike classical estimates, works for totally arbitrary (including discontinuous) distributions, and is based on recent one-sided strong laws of large numbers.

Original languageEnglish
Pages (from-to)45-53
Number of pages9
JournalStatistics and Probability Letters
Issue number1
StatePublished - 15 Aug 1996


  • Confidence intervals
  • Empirical distribution function
  • One-sided strong laws
  • Order statistics
  • Quantile estimators
  • Quantile intervals
  • Quantiles
  • Strongly-consistent estimators


Dive into the research topics of 'Strongly-consistent, distribution-free confidence intervals for quantiles'. Together they form a unique fingerprint.

Cite this