From: francogrex <francogrex_at_mail.com>

Date: Fri 05 Jan 2007 - 12:54:50 GMT

Date: Fri 05 Jan 2007 - 12:54:50 GMT

Hi Guys, it would be great if you could help me with a MLE problem in R.

I am trying to evaluate the maximum likelihood estimates of theta = (a1,
b1, a2, b2, P) which defines a mixture of a Poisson distribution and two
gamma prior distributions (where the Poisson means have a gamma
distribution, actually 2 gammas and P is the mixing factor). The likelihood
function for theta is L(theta) = Pi,j{P f(Nij; a1, b1, Eij) + (1 â€“ P) f(Nij;
a2, b2, Eij),}

The maximum likelihood estimate of theta is the vector that maximizes the
above equation (the values of N and E are given). The authors of the article
I read say that the maximization involves an iterative search in the five
dimensional parameter space, where each iteration involves computing
log[L(theta)] and its first and second-order derivatives. In test runs it is
suggested that the maximization typically takes between 5 and 15 iterations
from the starting point theta = (a1 = 0.2, b1 = 0.1, a2 = 2, b2 = 4, P =
1/3).

Now I have done maximization of a gamma-poisson mixture before (1 poisson, 1 gamma) successfully and I could determine correctly alpha (a) and beta(a). But this one above is giving me ridiculously large unusable values (for example P should not be above 1 and sometimes I get values of 500!) or even negative values! I know the values I should be obtaining with my samples shouldn't be far from the staring points. Is there a way to help me solve this issue? Thanks.

-- View this message in context: http://www.nabble.com/maximum-likelihood-estimation-of-5-parameters-tf2925364.html#a8177473 Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.Received on Fri Jan 05 23:59:57 2007

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