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st: RE: RE: Parameter restrictions in MLE


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: Parameter restrictions in MLE
Date   Fri, 26 May 2006 10:28:56 +0100

Maarten's answer is deeper than mine. However, 
if your model is at all appropriate this trick 
might work. 

Nick 
n.j.cox@durham.ac.uk 

Nick Cox
 
> I don't know about "best", particularly in general. 
> 
> But one way to do it is to parameterise in terms 
> of e.g. a, b, 1 - (a + b). If you are lucky this 
> will be enough. 
> 
> You could still have problems if the algorithm 
> is happiest at e.g. a = 1.1, b = -0.9, c = 0.8. 
 
Deepankar Basu
  
> > I am trying to do a maximum likelihood estimation where some of the
> > parameters of the model are probabilities; hence they 
> should add up to
> > unity. 
> > 
> > What is the best way to put restrictions on the relevant 
> > parameters and
> > constrain them to add up to unity?

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