Constrained non linear regression using ML

[Corrado]

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 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