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Thread Subject:
non-mse error criterion for linear regression

Subject: non-mse error criterion for linear regression

From: Aseman

Date: 1 Feb, 2013 02:01:59

Message: 1 of 2

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)

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

Thank you

Subject: non-mse error criterion for linear regression

From: Greg Heath

Date: 14 Oct, 2013 22:03:07

Message: 2 of 2

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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