If, for example, normality assumption holds then by doing robust regression instead of OLS you lose efficiency. So, it's not the same result after all. But you can do both, compare and decide. If robust regression produces results which are not really different from the OLS then stay with OLS.
On Fri, 1 Mar 2002, Rich Ulrich wrote: > On 1 Mar 2002 00:36:01 -0800, [EMAIL PROTECTED] (Alex Yu) > wrote: > > > > > I know that robust regression can downweight outliers. Should someone > > apply robust regression when the data have skewed distributions but do not > > have outliers? Regression assumptions require normality of residuals, but > > not the normality of raw scores. So does it help at all to use robust > > regression in this situation. Any help will be appreciated. > > Go ahead and do it if you want. > > If someone asks (or even if they don't), you can tell > them that robust regression gives exactly the same result. > > > -- > Rich Ulrich, [EMAIL PROTECTED] > http://www.pitt.edu/~wpilib/index.html > ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =================================================================
