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Posted to issues@spark.apache.org by "Patrick Wendell (JIRA)" <ji...@apache.org> on 2014/09/04 02:11:52 UTC
[jira] [Commented] (SPARK-3188) Add Robust Regression Algorithm
with Tukey bisquare weight function (Biweight Estimates)
[ https://issues.apache.org/jira/browse/SPARK-3188?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14120736#comment-14120736 ]
Patrick Wendell commented on SPARK-3188:
----------------------------------------
Hey [~fjiang6] please don't set the fix version on an issue until it is actually merged. Also, in general this field set by the committers and not contributors.
> Add Robust Regression Algorithm with Tukey bisquare weight function (Biweight Estimates)
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-3188
> URL: https://issues.apache.org/jira/browse/SPARK-3188
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.0.2
> Reporter: Fan Jiang
> Priority: Critical
> Labels: features
> Original Estimate: 0h
> Remaining Estimate: 0h
>
> Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression to employ a fitting criterion that is not as vulnerable as least square.
> The Tukey bisquare weight function, also referred to as the biweight function, produces an M-estimator that is more resistant to regression outliers than the Huber M-estimator (Andersen 2008: 19).
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