[RsR] Questions about interpreting lmRob output
Kjell Konis
kon|@ @end|ng |rom @t@t@@ox@@c@uk
Wed Nov 14 17:08:46 CET 2007
The basic idea underlying the robust linear model is that some
fraction (1-alpha > 0.5) of the data is distributed conditionally
normal and the remaining fraction (alpha) comes from some arbitrary
distribution (i.e., the outliers). The goal of a robust method is to
estimate the parameters (beta and sigma^2) of this conditional normal
distribution without giving the outliers too much influence. If the
bulk of the data (aka the good data) is not distributed conditionally
normal then a linear model is not appropriate regardless of whether it
is fit robustly or not. Of course you can still use all of the
standard linear modeling tricks. For instance a log transformation of
the response sometimes helps with heteroskedasticity.
Kjell
On 14 Nov 2007, at 15:24, Jenifer Larson-Hall wrote:
> Thanks so much Kjell. Your response answers most of my questions.
> Actually, I figured the overlaid plots things out (and the cool
> fit.models function) by looking through the archives and finding
> your pdf presentation that showed it (www.stats.ox.ac.uk/~konis/robust/ROBCLA2006-konis.pdf)
> . That was very helpful!
>
> The documentation you sent me privately (Robust.pdf, documentation
> for S-PLUS library) was helpful in clearing up a few more lingering
> questions (I guess if others want it they can email you).
The Robust Library Users Guide (Robust.pdf) is included in the source
version of the Robust Library.
> Just one more question now:
>
> My sense of robust methods was that they returned values which did
> not make strict normality and homogeneity of variances assumptions.
> In the data set I gave in my previous email, there is
> heteroskedasticity and non-normality distribution of data. So from
> what I understand from my reading, robust methods will give me a
> better sense of what's going on in the bulk of my data than least-
> squares estimates. If this is true, then what is the reason for
> looking at diagnostic plots? If I find the data is still
> heteroskedastic and non-normal in the plots after the robust
> analysis, is this cause for worry?
>
>
> Dr. Jenifer Larson-Hall
> Assistant Professor of Linguistics
> University of North Texas
> (940)369-8950
>
>
>
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