This is addressed to Prof Jenkins and anyone else who
has included large-dimensional calculations mvnp
calculations in a likelihood evaluator:
I've been using mvnp in a maximum likelihood
estimation and am finding that convergence is
extremely slow. I am doing a selection model with
multiple cases each involving an mvnp calculation of
different degrees. The three cases each have two-,
three-, and four-variate calls to mvnp, each with an
independent Cholesky matrix.
Because some of the cases involve several more error
terms that are independent of those in the mvnp
calculation, I also have to add several ln(normal(-x))
terms to the calculation.
Convergence is extremely slow and the elements of the
Cholesky matrix take on some outlandishly large values
in the process.
I am using ml rather than d0 since the log likelihood
is simply a sum of the individual likelihoods. I'm
also using technique(nr dfp) as you did for a
different problem in your 2006 paper.
Prof Jenkins, has this happened in your experience, or
do you think there is something wrong with the
likelihood evaluator?
Thank you in advance for your help.
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