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[RsR] Questions about interpreting lmRob output

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:

            
The Robust Library Users Guide (Robust.pdf) is included in the source  
version of the Robust Library.