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From "Venkatesha Murthy TS (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MATH-418) add a storeless version of Percentile
Date Sat, 19 Apr 2014 21:29:16 GMT

    [ https://issues.apache.org/jira/browse/MATH-418?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13974976#comment-13974976
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Venkatesha Murthy TS commented on MATH-418:
-------------------------------------------

Ted ,
I understand the point of view ; however just to clarify
a) While  I have modeled this implement based on the existing Percentile class and as StorelessUnivariatestatistic;
i agree its approximate for smaller sets but should improve for larger set. I could add a
comment that this is an approximate technique,
b) GK, T-Digest, Q-Digest, BinMedian, BinApprox etc i feel all of them can be implementation
choices that user could use rather than sticking to any perticular algo

Please let me know .

Phil,
Also l will clear the check style and findbug issues and re submit with tests added



> add a storeless version of Percentile
> -------------------------------------
>
>                 Key: MATH-418
>                 URL: https://issues.apache.org/jira/browse/MATH-418
>             Project: Commons Math
>          Issue Type: New Feature
>    Affects Versions: 2.1
>            Reporter: Luc Maisonobe
>             Fix For: 4.0
>
>         Attachments: patch
>
>
> The Percentile class can handle only in-memory data.
> It would be interesting to use an on-line algorithm to estimate quantiles as a storeless
statistic.
> An example of such an algorithm is the exponentially weighted stochastic approximation
 described in a 2000 paper by Fei Chen ,  Diane Lambert  and José C. Pinheiro "Incremental
Quantile Estimation for Massive Tracking" which can be retrieved from CiteSeerX at [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.1580].



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