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

Authors:
John C. Liechty;Dennis K. J. Lin;James P. McDermott
Affiliations:
Department of Marketing, Pennsylvania State University, University Park, PA 16802, USA. jcl12@psu.edu;Department of Supply Chain and Information Systems, University Park, PA 16802, USA. dk15@psu.edu;Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA. mcdermott@stat.psu.edu
Venue:
Statistics and Computing
Year:
2003

Citing 0
Cited 2

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Abstract

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.