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https://issues.apache.org/jira/browse/MATH-607?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13060814#comment-13060814
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Phil Steitz commented on MATH-607:
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I did not see the parameter covariance matrix in RegressionResults. I agree
with your basic point on this, though. I am less concerned with wanting to add
stuff than including things that we either wish we had omitted (e.g. the
redundancy stuff as just an example) or typed or constrained differently. How
about starting with a minimalist concrete class and once we have the interface
stabilized, we can peel it off and keep the class for persisting / serializing
results.
Sorry to flip/flop, but looking carefully at the UpdatingLinearRegression
interface again, I think it is fine to just add it as an interface. I would
suggest the s/data/observation change in my last comment though and maybe
renaming it to UpdatingMultipleLinearRegression.
> Current Multiple Regression Object does calculations with all data incore.
> There are non incore techniques which would be useful with large datasets.
> -----------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: MATH-607
> URL: https://issues.apache.org/jira/browse/MATH-607
> Project: Commons Math
> Issue Type: New Feature
> Affects Versions: 3.0
> Environment: Java
> Reporter: greg sterijevski
> Labels: Gentleman's, QR, Regression, Updating, decomposition,
> lemma
> Fix For: 3.0
>
> Attachments: updating_reg_ifaces
>
> Original Estimate: 840h
> Remaining Estimate: 840h
>
> The current multiple regression class does a QR decomposition on the complete
> data set. This necessitates the loading incore of the complete dataset. For
> large datasets, or large datasets and a requirement to do datamining or
> stepwise regression this is not practical. There are techniques which form
> the normal equations on the fly, as well as ones which form the QR
> decomposition on an update basis. I am proposing, first, the specification of
> an "UpdatingLinearRegression" interface which defines basic functionality all
> such techniques must fulfill.
> Related to this 'updating' regression, the results of running a regression on
> some subset of the data should be encapsulated in an immutable object. This
> is to ensure that subsequent additions of observations do not corrupt or
> render inconsistent parameter estimates. I am calling this interface
> "RegressionResults".
> Once the community has reached a consensus on the interface, work on the
> concrete implementation of these techniques will take place.
> Thanks,
> -Greg
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