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https://issues.apache.org/jira/browse/MATH-418?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13685716#comment-13685716
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Ajo Fod commented on MATH-418:
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Any solution is better than the current situation. However, there are two
desirable features.
Reducability: Since large data datasets typically are associated with parallel
environments, the ideal algorithm would be map/reduce-able.
Heteroskedasticity: What if one passes it the quantiles of say an exponential
distribution not randomly but in sequential order to the quantile estimator?
How big would the quantile error be? Is it significantly more than the iid case?
> 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
>
>
> 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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