[R] problems with numerical optimisation
spencer.graves at pdf.com
Wed Mar 12 16:45:56 CET 2003
Do you compute the singular value decomponsition of your gradients?
Unless you compute a marginal likelihood using Monte Carlo integration,
I would expect convergence problems to be evident in the range of the
absolute values of the singular values of the gradients: If the
smallest is less than, say, 1e-15 times the largest, you are chasing
round-off, as discussed years ago by Box, Hunter, MacGregor, and Erjavec
(1973) "Some problems associated with the analysis of multiresponse
data", Technometrics, 15: 33-51.
(If you are using Monte Carlo integration, then I would ask if you've
considered Markov Chain Monte Carlo?)
Hope this helps,
Ott Toomet wrote:
> Dear list,
> this is not a particular R question but perhaps someone can help.
> I am running a maximum likelihood estimation (competing risk duration
> model with unobserved heterogeneity) on 30 different datasets. The
> problem is that on 2 datasets the model does not converge. I am
> interested if there are any methods, based on the gradients or (an
> approximation of) the hessian which helps to determine what is the
> problem. Can anybody recommend a good textbook about numerical
> Currently I am using 100 BFGS iterations + 100 BHHH iterations and I
> have programmed analytic gradients. The fool-proof method of
> excluding the variables one-by-one and simplifying the structure is
> quite a slow and not particularily insightsful.
> Thanks in advance
> R-help at stat.math.ethz.ch mailing list
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