Dear All,
I am working with skewed-t copula in my research recently, so I needed to
write an mle
procedure instead of using a standard fit one; I stick to the sn package. On
subsamples of the entire population that I deal with, everything is fine.
However, on the total sample (difference in cross-sectional
dimension: 30 vs 240) things go wrong - the objective function diverges to
infinity. I located the "rotten" line
to be

t1 <- dmst(vector, mu, P, alpha, nu)

where "vector" is the matrix row, on which I evaluate my likelihood and the
rest in parametrized in a standard
way, just as the help pages give it. In large dimensions, I get a zero value
of the density (which is probably due to numerical issues). I tried the
following dummy example

t1 <- rmst(1,mu,P,alpha, nu)
t2 <- dmst(t1, mu, alpha,nu)

and t2 remains to be zero. Can anyone help me on this one?

thanks in advance,
Konrad

--
"We are what we pretend to be, so we must be careful about what we pretend
to be"

                      Kurt Vonnegut Jr. "Mother Night"

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