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Thread: Determining residual outliers when heteroskedasticity exists

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    Determining residual outliers when heteroskedasticity exists




    Hi,

    I was wondering if anyone has any thoughts on the best way to approach the detection of outliers based on residuals from a linear regression when heteroskedasticity exists. For example, if the variance in the upper end of the the dependent variable increases with an increase in an independent variable, then calculating the Z-score for a particular residual that refers to a datapoint in relation to all others does not seem to make sense in this instance.

    Any thoughts?

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    Re: Determining residual outliers when heteroskedasticity exists

    Side question, if you have heteroscedasticity why are you worried about outliers and moving forward instead of addressing that assumption of linear regression?
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    Re: Determining residual outliers when heteroskedasticity exists

    Quote Originally Posted by hlsmith View Post
    Side question, if you have heteroscedasticity why are you worried about outliers and moving forward instead of addressing that assumption of linear regression?
    I guess the assumption here could be that outliers in the data cause the heteroskedastisity. If this is the case, then a plot of residuals vs. variables in the model might help reveal which specific variables cause the issues.

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    Re: Determining residual outliers when heteroskedasticity exists


    I need to visualize these things but if there was a nonlinear pattern in the residuals your comment could come into play, perhaps?
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