Single-pass low-storage arbitrary quantile estimation for massive datasets

John C. Liechty, Dennis K.J. Lin, James P. McDermott

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


We present a single-pass, low-storage, sequential method for estimating an arbitrary quantile of an unknown distribution. The proposed method performs very well when compared to existing methods for estimating the median as well as arbitrary quantiles for a wide range of densities. In addition to explaining the method and presenting the results of the simulation study, we discuss intuition behind the method and demonstrate empirically, for certain densities, that the proposed estimator converges to the sample quantile.

Original languageEnglish (US)
Pages (from-to)91-100
Number of pages10
JournalStatistics and Computing
Issue number2
StatePublished - Apr 2003

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics


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