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https://issues.apache.org/jira/browse/SPARK-3188?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Fan Jiang updated SPARK-3188:
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Description:
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).
was:
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 Turkey 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).
> 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
> Fix For: 1.1.1, 1.2.0
>
> 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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