Statistics – Computation
Scientific paper
May 2002
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002aas...200.6004m&link_type=abstract
American Astronomical Society, 200th AAS Meeting, #60.04; Bulletin of the American Astronomical Society, Vol. 34, p.742
Statistics
Computation
Scientific paper
Due to the massive amounts of data to be stored in a Virtual Observatory, new non-traditional statistical methods are required to process the data efficiently. 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. This work is produced by an interdisciplinary collaborative effort supported by the NSF FRG grant DMS-0101360.
Babu Gutti Jogesh
Feigelson Eric D.
McDermott James P.
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