[ https://issues.apache.org/jira/browse/SPARK-3188?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Fan Jiang updated SPARK-3188: ----------------------------- 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 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). 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 and M-estimator that is more resistant to regression outliers than the Huber M-estimator (Andersen 2008: 19). > Add Robust Regression Algorithm with Turkey 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 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). -- This message was sent by Atlassian JIRA (v6.2#6252) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org