If the positive definiteness of the covariance
is the only issue, then you could base a penalty on:
eps - smallest.eigen.value
if the smallest eigen value is smaller than eps.
Patrick Burns
[EMAIL PROTECTED]
+44 (0)20 8525 0696
http://www.burns-stat.com
(home of S Poetry and "A Guide for the Unwilling S User")
Mathieu Ribatet wrote:
Thanks Ben for your tips.
I'm not sure it'll be so easy to do (as the non-feasible regions
depend on the model parameters), but I'm sure it's worth giving a try.
Thanks !!!
Best,
Mathieu
Ben Bolker a écrit :
Mathieu Ribatet <mathieu.ribatet <at> epfl.ch> writes:
Dear list,
I'm currently writing a C code to compute the (composite) likelihood -
well this is done but not really robust. The C code is wrapped in an R
one which call the optimizer routine - optim or nlm. However, the
fitting procedure is far from being robust as the parameter space
depends on the parameter - I have a covariance matrix that should be a
valid one for example.
One reasonably straightforward hack to deal with this is
to add a penalty that is (e.g.) a quadratic function of the
distance from the feasible region, if that is reasonably
straightforward to compute -- that way your function will
get gently pushed back toward the feasible region.
Ben Bolker
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