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Re: Constrained non linear regression using ML
From: |
Jaroslav Hajek |
Subject: |
Re: Constrained non linear regression using ML |
Date: |
Wed, 17 Mar 2010 11:05:20 +0100 |
On Tue, Mar 16, 2010 at 8:01 PM, Corrado <address@hidden> wrote:
> Dear Octave users,
>
> I have to fit the non linear regression:
>
> y~1-exp(-(k0+k1*p1+k2*p2+ .... +kn*pn))
>
> where ki>=0 for each i in [1 .... n] and pi are on R+.
>
> I am using, at the moment, nls, but I would rather use a Maximum
> Likelhood based algorithm. The error is not necessarily normally
> distributed.
>
You should have an idea of an alternative error distribution, then. Do you?
In any case I'd start from transforming
x = -log1p (-y);
and fitting the linear nonnegative model
x ~ k0 + k1*p1 + ...
as an initial guess. But maybe you already do that? It is even
possible that the error distribution in x will be closer to normal
than in z. If this makes a good initial guess,
expectation-maximization can bring you to the ML estimate with little
effort, but you still need to make a parametrized assumption about the
error distribution.
> --
> Corrado Topi
> PhD Researcher
> Global Climate Change and Biodiversity
> Area 18,Department of Biology
> University of York, York, YO10 5YW, UK
> Phone: + 44 (0) 1904 328645, E-mail: address@hidden
>
>
> _______________________________________________
> Help-octave mailing list
> address@hidden
> https://www-old.cae.wisc.edu/mailman/listinfo/help-octave
>
--
RNDr. Jaroslav Hajek, PhD
computing expert & GNU Octave developer
Aeronautical Research and Test Institute (VZLU)
Prague, Czech Republic
url: www.highegg.matfyz.cz
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Re: Constrained non linear regression using ML,
Jaroslav Hajek <=
Re: Constrained non linear regression using ML, Dupuis, 2010/03/17