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Re: st: non-linearities in ml
From
Michael Barker <[email protected]>
To
[email protected]
Subject
Re: st: non-linearities in ml
Date
Wed, 31 Jul 2013 10:16:03 -0400
"lf" can be used anytime the likelihood function can be written as the
sum of independent observations. It does not mean that your likelihood
function should be linear in parameters.
The way you've written the likelihood function above, it looks like
the lf model is appropriate.
Mike
On Tue, Jul 30, 2013 at 10:21 PM, Ishani Tewari <[email protected]> wrote:
> Dear all,
> I need to estimate a logit where the parameters (beta1,beta2,beta3)
> enter very non-linearly:.
>
> lnf=ln(invlogit((X1^beta1')*(`beta2*X2+`beta3'*log(`beta3'/X1)))) if y1==1
> lnf=ln(invlogit(-(X1^beta1')*(`beta2*X2+`beta3'*log(`beta3'/X1)))) if y1==0
>
> Can I (should I) still use the "lf" model?
>
> thnks
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