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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:
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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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