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Date: Sat, 20 Jun 2009 06:46:07 -0700 (PDT)
From: "Luc Maisonobe (JIRA)" <jira@apache.org>
To: issues@commons.apache.org
Subject: [jira] Resolved: (MATH-278) Robust locally weighted regression
 (Loess / Lowess)
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     [ https://issues.apache.org/jira/browse/MATH-278?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Luc Maisonobe resolved MATH-278.
--------------------------------

    Resolution: Fixed
      Assignee: Luc Maisonobe

solved in subversion repository as of r786821
applied patch with minor changes
thanks for the patch

> Robust locally weighted regression (Loess / Lowess)
> ---------------------------------------------------
>
>                 Key: MATH-278
>                 URL: https://issues.apache.org/jira/browse/MATH-278
>             Project: Commons Math
>          Issue Type: New Feature
>            Reporter: Eugene Kirpichov
>            Assignee: Luc Maisonobe
>         Attachments: loess.patch, loess.patch.v2
>
>
> Attached is a patch that implements the robust Loess procedure for smoothing univariate scatterplots with local linear regression ( http://en.wikipedia.org/wiki/Local_regression) described by William Cleveland in http://www.math.tau.ac.il/~yekutiel/MA%20seminar/Cleveland%201979.pdf , with tests.
> (Also, the patch fixes one missing-javadoc checkstyle warning in the AbstractIntegrator class: I wanted to make it so that the code with my patch does not generate any checkstyle warnings at all)
> I propose to include the procedure into commons-math because commons-math, as of now, does not possess a method for robust smoothing of noisy data: there is  interpolation (which virtually can't be used for noisy data at all) and there's regression, which has quite different goals. 
> Loess allows one to build a smooth curve with a controllable degree of smoothness that approximates the overall shape of the data.
> I tried to follow the code requirements as strictly as possible: the tests cover the code completely, there are no checkstyle warnings, etc. The code is completely written by myself from scratch, with no borrowings of third-party licensed code.
> The method is pretty computationally intensive (10000 points with a bandwidth of 0.3 and 4 robustness iterations take about 3.7sec on my machine; generally the complexity is O(robustnessIters * n^2 * bandwidth)), but I don't know how to optimize it further; all implementations that I have found use exactly the same algorithm as mine for the unidimensional case.
> Some TODOs, in vastly increasing order of complexity:
>  - Make the weight function customizable: according to Cleveland, this is needed in some exotic cases only, like, where the desired approximation is non-continuous, for example.
>  - Make the degree of the locally fitted polynomial customizable: currently the algorithm does only a linear local regression; it might be useful to make it also use quadratic regression. Higher degrees are not worth it, according to Cleveland.
>  - Generalize the algorithm to the multidimensional case: this will require A LOT of hard work.

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