In article <[EMAIL PROTECTED]>,
[EMAIL PROTECTED] (Vitaly Kupisk) wrote:

>Subject: Re: orthogonal linear regression
>From: [EMAIL PROTECTED] (Vitaly Kupisk)
>Organization: http://groups.google.com/
>Date: 4 Jan 2003 14:07:18 -0800
>Newsgroups: sci.stat.math,sci.stat.edu
>
>I have (tried to) express that geometric idea algebraically; yes,
>geometrically I mean that the residuals are measured orthogonally to
>the fitted hyperplane (I am not fitting a fancier hypersurface, so the
>rotation for all residuals is the same, the angle between the "y" axis
>and the normal to the fitted hyperplane, right?).  The formula also
>seems to match the one (formula (1)) in the Bargiela/Hartley paper,
>www.doc.ntu.ac.uk/RTTS/Papers/rttg-publ02.ps.
>If you see an error or know of a different-looking expression for the
>orthogonal L1 distance, please let me know.
>
>I would also very much like to get more details on the "reweighted L2"
>implementaion -- it seems close to what the Bargiela/Hartley paper is
>talking about, which was my question that started the thread.  Do you
>have any references or a description of the method?


Ask google! 

"iteratively reweighted least squares" gives 1520 hits.


>Thanks,
>
>Vitaly
>
>
>[EMAIL PROTECTED] (Gordon Sande) wrote in message
>news:<[EMAIL PROTECTED]>...
>> ... Given the rather uncertain terminology in use here, it
>> might be the case that iterative reweighting with ODRPack will do 
>> your job as at first sight your definition does not match what I 
>> understand by the usual "orthogonal" regression. The usual 
>> "orthogonal" regression has a different rotation for each residual 
>> so the residual is orthogonal to the fitted curve. Maybe that is
>> what you actually want but have not quite formulated it that way.




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