Drexel dragonThe Math ForumDonate to the Math Forum

Search All of the Math Forum:

Views expressed in these public forums are not endorsed by Drexel University or The Math Forum.

Math Forum » Discussions » Software » comp.soft-sys.matlab

Topic: non-mse error criterion for linear regression
Replies: 1   Last Post: Oct 14, 2013 6:03 PM

Advanced Search

Back to Topic List Back to Topic List Jump to Tree View Jump to Tree View   Messages: [ Previous | Next ]
Greg Heath

Posts: 6,263
Registered: 12/7/04
Re: non-mse error criterion for linear regression
Posted: Oct 14, 2013 6:03 PM
  Click to see the message monospaced in plain text Plain Text   Click to reply to this topic Reply

Aseman <andalibar@gmail.com> wrote in message <cfe4cd27-cb78-479f-bae8-9fa815bf8654@googlegroups.com>...
> hi
> Consider a robust regression problem like this
> x = (-1:0.02:1)';
> y = x+0.9*normrnd(0,0.1,length(x),1)+0.1*normrnd(4,0.1,length(x),1);
> brob = robustfit(x,y)

Try replacing the first term of y with a*x+b

> I belive that both regress and robustfit employ mean square error. How can I used a different error criterion to solve the same problem?

If either of these do not have a weighting option, consider

help lscov
doc lscov

Otherwise consider a neural network with 0 (or more) hidden nodes. Options include
combinations of weighting, MSE, SSE, MAE and SAE.

Hope this helps


Point your RSS reader here for a feed of the latest messages in this topic.

[Privacy Policy] [Terms of Use]

© Drexel University 1994-2015. All Rights Reserved.
The Math Forum is a research and educational enterprise of the Drexel University School of Education.