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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2014/11/02 20:01:34 UTC
[jira] [Updated] (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:all-tabpanel ]
Xiangrui Meng updated SPARK-3188:
---------------------------------
Target Version/s: 1.3.0 (was: 1.2.0)
> 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
> Reporter: Fan Jiang
> Priority: Minor
> 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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