> do I choleski decompose
> the inverse of the covariance matrix and weight the observations -
> risking precision loss.
- I think you'd be better off choleski decomposing the cov matrix itself
wouldn't you? e.g. if V is the covariance matrix use chol() to get
V=L^T L
and then form L^{-T}y and L^{-T}X using solve() (assuming model is
y=Xb+e).
Simon
_____________________________________________________________________
> Simon Wood [EMAIL PROTECTED] www.stats.gla.ac.uk/~simon/
>> Department of Statistics, University of Glasgow, Glasgow, G12 8QQ
>>> Direct telephone: (0)141 330 4530 Fax: (0)141 330 4814
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