[R] Skewed t distribution

[Prof Brian Ripley]

Try maximizing the log-likelihood and using the log=TRUE argument to dmst.

(You have told us so little about what you are doing that we can but guess 
at what you mean by `write an mle procedure': what is wrong with st.mle, 
for example?)

On Tue, 28 Mar 2006, Konrad Banachewicz wrote:

> 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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>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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