--- marcel spijkerman <[email protected]> wrote:
> I am estimating by ML a likelihood of the form:
>
> L = ln(p1*L1 + p2*L2 + p3*L3 + p4*L4)
>
> where the p's are mixture probabilities and the L's are likelihood
> contributions of a survival density. now I want to estimate the
> parameters of this likelihood by the EM - algorithm, and therefore I
> need to compute the likelihood contribution for each individual and
> each term given de data and current parameter estimates. My question
> is how I can do this effeciently, thus given e(b) from a previous
> step, computing the likelihood contributions for each term, use these
> to upfdate the p's and estimate the parameters again?
There is no nicely packaged suite of commands that will help you do a
EM, so you'll have to do it yourself. Given that, your first concern
should be to get it working correctly. Once you've done that you can
start worrying about efficiency. Basically you'll want a -while- loop.
Call the parameters of L1, L2, etc, b, than below will give you a rough
sketch of such a program.
Hope this helps,
Maarten
*----------------- begin sketch -----------------
local `i' = 1
while `critll' > `emltolerance' & ///
`critb' > `emtolerance' & ///
`i' < `emiterate'{
scalar `old_ll' = r(new_ll)
matrix `old_b' = r(init)
Ls `varlist' `wgt' if `touse', other options
Ps `varlist' `wgt' if `touse', other options
scalar `critll' = reldif(r(new_ll),`old_ll')
scalar `critb' = mreldif(r(init),`old_b')
local ++i
}
program define Ps, rclass
maximize likelihood with respect to b, while fixing p
end
program define Ps, rclass
maximize likelihood with respect to p1, p2, etc while fixing bs
end
*----------------- end sketch -----------------
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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