Abstract #301152

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JSM 2002 Abstract #301152
Activity Number: 52
Type: Contributed
Date/Time: Sunday, August 11, 2002 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Computing*
Abstract - #301152
Title: Low-Storage, Sequential, Simultaneous Estimation of Multiple Quantiles for Massive Datasets
Author(s): James McDermott*+ and John Liechty and Dennis Lin
Affiliation(s): Pennsylvania State University and Pennsylvania State University and Pennsylvania State University
Address: 325 Thomas Bldg, University Park, Pennsylvania, 16802, USA
Keywords: quantile estimation ; sequential methods ; massive datasets ; low-storage ; datamining ; data mining
Abstract:

We propose a low-storage, single-pass, sequential method for simultaneous estimation of multiple quantiles for massive datasets. The proposed method uses estimated ranks, assigned weights, and a scoring function that determines the most attractive candidate data points for estimates of the quantiles. The method uses a small fixed amount of storage and its computation time is O(n). Asymptotically, the proposed estimates are as accurate as the sample quantiles. We compare the proposed method's performance with that of the empirical distribution function through simulation study.

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