This raises the old classical trope,
beaten almost to death by the late Sir Isaiah Berlin
in many of his essays on intellectual history,
that the fox knows many things, but the hedgehog
knows one big thing.
When attacked, the hedgehog has just one means
of defence, although it is usually effective. -qreg-
is a hedgehog. The fox has many different tricks. -rreg-
is a fox. Its mixed strategy is an attempt to be smart
in different ways.
My experience loosely matches Richard's, certainly
in terms of wanting to think that -qreg- is as good
because of the much greater ease in explaining it.
At the same time, if you have outliers everywhere,
you are possibly working on inappropriate scales
and should wonder about reaching for a transformation
or, in some frameworks, a different link function.
Nick
[email protected]
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]]On Behalf Of Richard
> Williams
> Sent: 30 January 2004 13:20
> To: [email protected]
> Subject: st: qreg versus rreg
>
>
> This came up several months ago on the list but I am still
> confused: As a
> means for dealing with outliers, what are the relative merits
> of -rreg- and
> -qreg-? When should one be preferred over the other?
>
> As I understand it, -rreg- goes through this complicated
> weighting scheme,
> which causes outliers to be weighted less heavily. -qreg-
> (by default)
> does median regression, and the median is less affected by
> outliers than
> the mean is.
>
> In terms of giving a quick 30 second intuitive explanation, I like
> -qreg-. On the other hand, in the few examples I've tried
> myself or seen
> elsewhere, the results from -rreg- seemed more plausible. On
> the other
> other hand, in those examples -rreg- basically just dropped
> the extreme
> outliers, and I could do that myself without a fancy program.
>
> This is one of the problems with using Stata in a stats
> class. When I only
> used SPSS, these issues never came up, because as far as I
> can tell SPSS
> can't do anything like this!
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