Re: Constrained non linear regression using ML

[Fredrik Lingvall]

On 03/16/10 20:01, Corrado 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.
>
> y is approximately beta distributed, and the volume of data is medium to
> large (the y,pi may have ~ 40,000 elements).
>
> Any suggestion?
>
> Regards
>   
Corrado,

Can you tell us a little more about your problem?

As I understand it you have a model,

y = 1 - exp(-k'*p) + e

where k = [k_0 k_1 ... k_n]' and p = [1 p_1 p_2 ... p_n]' and where y is
your data vector, p is your "input signal" and k is the parameter vector
of your model. Have I understood you correctly?

/Fredrik