[R] Skewed t distribution
Prof Brian Ripley
ripley at stats.ox.ac.uk
Tue Mar 28 12:14:39 CEST 2006
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,
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,
> "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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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)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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