Statistics and Computing

, Volume 13, Issue 2, pp 91–100 | Cite as

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

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


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.

low-storage quantile estimation single-pass algorithms data mining large datasets tail quantile 


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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • John C. Liechty
    • 1
  • Dennis K. J. Lin
    • 2
  • James P. McDermott
    • 3
  1. 1.Department of MarketingPennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of Supply Chain and Information SystemsUniversity ParkUSA
  3. 3.Department of StatisticsPennsylvania State UniversityUniversity ParkUSA

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