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Re: st: RE: RE: Parameter restrictions in MLE


From   Deepankar Basu <basu.15@osu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: RE: Parameter restrictions in MLE
Date   Fri, 26 May 2006 12:52:41 -0400

Maarten and Nick, 
Thanks for your comments. Just now, I came across the "constraints"
command which can be used to specify linear constraints on the
parameters. Since my constraints are linear, I can probably use this
too. 
Deepankar

On Fri, 2006-05-26 at 10:28 +0100, Nick Cox wrote:
> 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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