posted and e-mailed.

On 24 Sep 2003 14:49:14 -0700, [EMAIL PROTECTED] (kjetil brinchmann
halvorsen) wrote:

> On 24 Sep 2003 at 16:23, Rich Ulrich wrote:
> 
[ snip, some]
RU > > 
> > On (1).  What sort of 'nonlinear' ? 
> > Engineers and psychologists often include simple polynomials 
> > when they say 'nonlinear', since these can draw curved lines;
> > whereas statisticians are concerned with nonlinear-in-the-
> > coefficients, which are more complicated.  
> > 
> > So, do you mean the easy nonlinear, or the tough one? 

KB > 
> That was the state some time ago non-linear -in-coefficients models
> are not more any more difficult than linear models, there is a lot of 
> high-quality optimization software around now! If your statistics 
> package continues to make it difficult, or you does'n need it, or you 
> change package. It is time to start to use the more natural 
> terminology you say engeeneers and psychologists use. Some well-known 
> statisticians have also said so publically, like Jim Lindsey, which 
> uses the terminology that way in his books and on-line documents. 
> Algorithmic difficulties should not determine terminology any more. 

I found that idea interesting, the idea that what I called
"[tough] non-linear is no longer more difficult than linear."

But I think that the essential differences, unfortunately,
extent far beyond the ease of using a stat-package.

Yes, linear models *might*  have gross difficulties about
non-homogenous errors, and thus give you R-squared
that is impossible to use.  And tests that are unreliable.
But the linear model, very often, is close-enough-to-
correct that all those things are usable.  

But the appropriateness  is *always*  something to 
beware of, for nonlinear, unless you are following a
well-worn path of precedent in your own area, where
you can cite the authorities for using your choice of
setting up the parameters.  

-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
"Taxes are the price we pay for civilization." 
.
.
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