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" [[alternative HTML version deleted]] ______________________________________________ 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