On 6 October 2011 16:20, Dimitris.Kapetanakis
dimitrios.kapetana...@gmail.com wrote:
Dear all,
I would like to create a code for semiparametric Klein and Spady's
estimator. For that I created a function that provides the log-likelihood
function for each observation (so it is a function of betas and i, where i
denotes the observation). Now, in order to maximize the log-likelihood
function, I have to sum these log-likelihood functions for each i and so to
get another function that is a function only of betas and so to maximize it
through maxLik for instance. Is that possible?
In order to be more clear I give an example of how it could be:
Prob1 - function(b, i)
g.yj(b,y=1,h.np,i)/(g.yj(b,y=1,h.np,i)+g.yj(b,y=0,h.np,i))
loglik.i- function(b, i) Y[i,]*log(Prob1(b,i))+(1-Y[i,])*log(1-Prob1(b,i))
where b denotes the betas, i the observations, Y is the response vector and
g.yj(b,1,h.np,i) a function that I created previously, Prob1(b,i) is a
function that gives the conditional probability for observation i and
loglik.i(b,i) is function that gives the log-likelihood for observation i.
How can I sum the loglik.i(b,i) for each i and remain as a function of b
ONLY in order to maximize it???
For exemple this could be done manually by
loglik- function(b)
loglik.i(b,1)+loglik.i(b,2)+loglik.i(b,3)+….+loglik.i(b,N)
but how can I do it automatically for all observations?
loglik - function(b) sapply( 1:N, loglik.i, b = b )
Please note that logLik( b ) returns a *vector* of the likelihood
contributions of each observation. maxLik() takes the sum of the
elements of this vector automatically. If logLik( b ) returns a vector
of the likelihood contributions of each observation (rather than just
the sum), the BHHH optimisation method can be used.
/Arne
--
Arne Henningsen
http://www.arne-henningsen.name
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