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From: Patrick Alken <patrick.alken@Colorado.EDU>
To: "gsl-discuss@sourceware.org" <gsl-discuss@sourceware.org>
Subject: Robust linear least squares
Date: Fri, 10 May 2013 22:01:00 -0000	[thread overview]
Message-ID: <518D6E3B.8080503@colorado.edu> (raw)

Hi all,

   I just committed a significant chunk of code related to robust linear 
regression into GSL and mainly wanted to update the other developers and 
any other interested parties. The main idea here is that ordinary least 
squares is very sensitive to data outliers, and the robust algorithm 
tries to identify and downweight outlier points so they don't 
drastically affect the model. I think this is something that has been 
needed in gsl for a while.

   I've been developing the code for a while and have been using it 
successfully in my own work, and also validated it pretty extensively 
against the matlab implementation. I still need to make some automated 
tests for it which I should get to next week.

   In the meantime, the code is very usable and working so feel free to 
try it out.

Patrick

             reply	other threads:[~2013-05-10 22:01 UTC|newest]

Thread overview: 6+ messages / expand[flat|nested]  mbox.gz  Atom feed  top
2013-05-10 22:01 Patrick Alken [this message]
2013-05-12 17:56 ` Peter Teuben
2013-05-12 18:13   ` Dirk Eddelbuettel
2013-05-12 18:14   ` Patrick Alken
2013-05-24 19:23 Timothée Flutre
2013-05-24 20:12 ` Patrick Alken

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