[jira] [Updated] (MATH-857) Include a VIF and TOLERANCE check for a 2 dimensional double array, to determine variables that cause multi-colinearity issues and should be excluded from the models

["Gilles (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MATH-857?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Gilles updated MATH-857:
------------------------

             Priority: Minor  (was: Major)
    Affects Version/s:     (was: 3.1)
        Fix Version/s:     (was: 3.0)
              Summary: Include a VIF and TOLERANCE check for a 2 dimensional double array, to determine variables that cause multi-colinearity issues and should be excluded from the models  (was: I would like to include a VIF and TOLERANCE check for a 2 dimensional double array, vey commonly used by major packages to determine variables that cause multi-colinearity issues and should be excluded from the models)
    
> Include a VIF and TOLERANCE check for a 2 dimensional double array, to determine variables that cause multi-colinearity issues and should be excluded from the models
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: MATH-857
>                 URL: https://issues.apache.org/jira/browse/MATH-857
>             Project: Commons Math
>          Issue Type: New Feature
>    Affects Versions: 3.0
>         Environment: can apply to all operating systems
>            Reporter: Marios Michaelidis
>            Priority: Minor
>              Labels: build, test
>             Fix For: 3.1
>
>   Original Estimate: 48h
>  Remaining Estimate: 48h
>
> Multicollinearity is a statistical phenomenon in which two or more predictor variables in any multiple regression model are highly correlated. Tolerance and VIF are checks that allows to avoid optimization failes due to "inability to converge". Most of the times, the major packages (SAS, SPSS etc), have a check prior to running the model and they exclude variables that might cause these kind of problems. It is quite a useful tool to be in common maths.

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