+ Reply to Thread
Results 1 to 3 of 3

Thread: Tolerable Heteroskedasticity/ Variance Inhomogenity

  1. #1
    Points: 7,821, Level: 59
    Level completed: 36%, Points required for next Level: 129

    Posts
    159
    Thanks
    1
    Thanked 7 Times in 7 Posts

    Tolerable Heteroskedasticity/ Variance Inhomogenity




    Hello,

    a long time ago I have read somewhere, that a regression can overcome heteroskedasticity/ variance inhomogenity up to certain amount.

    It might have been working something like the variance is calculated for certain target variable intervals. If the highest variance was maximal m-times larger the lowest variance, on could suppose, that the calculated regression coefficients are still usable, or something like this. Unfortunately I do not find it again.

    Do you know this over the thumb rule regarding deviating variances?
    Prediction is very difficult, especially about the future. (Niels Bohr)

  2. #2
    Omega Contributor
    Points: 38,289, Level: 100
    Level completed: 0%, Points required for next Level: 0
    hlsmith's Avatar
    Location
    Not Ames, IA
    Posts
    6,992
    Thanks
    397
    Thanked 1,185 Times in 1,146 Posts

    Re: Tolerable Heteroskedasticity/ Variance Inhomogenity

    No source, but the residuals don't have to be perfectly homoskedastic and sometimes people use robust estimators. Not sure if it would matter if they are heteroskedastic, but appear to have a random element and not a distinct underlying pattern??

    Would be interested in seeing a citation for this as well. Have not heard of a rule of thumb.
    Stop cowardice, ban guns!

  3. #3
    Points: 7,821, Level: 59
    Level completed: 36%, Points required for next Level: 129

    Posts
    159
    Thanks
    1
    Thanked 7 Times in 7 Posts

    Maximum minimum quotient criterion


    I have found it again.

    It is the maximum minimum quotient criterion.

    Following Ryan (1997, S. 61) there is no need to be afraid of heteroskedacity, till the quotient out of maximum and minimum standard deviation is below 1.5, while quotient above 3.0 are inacceptable. That means, translated to variances, variance quotients above 9.0 are critical. Similar tell Cohen et al. (2003, S. 120) und Fox (1997, S. 306f), who state 10 as critical ratio between the maximal and minimal variance.


    Ryan, T.S. (1997). Modern Regression Methods. New York: Wiley.

    Cohen, J., Cohen, P., West, S.G. & Aiken, L. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.). Mahwah: Lawrence Erlbaum Associates.

    Fox, J. (1997). Applied Regression Analysis, Linear Models, and related Methods. Thousand Oaks: Sage.

    Prediction is very difficult, especially about the future. (Niels Bohr)

+ Reply to Thread

           




Posting Permissions

  • You may not post new threads
  • You may not post replies
  • You may not post attachments
  • You may not edit your posts






Advertise on Talk Stats