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

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Greg Heath

Posts: 5,912
Registered: 12/7/04
Re: non-mse error criterion for linear regression
Posted: Oct 14, 2013 6:03 PM
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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

Greg



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