Speaking of IML, it has several modules implementing nonlinear
optimization techniques. IML has the following methods: conjugate
gradient, double dogleg, Nelder-Mead simplex, Newton-Raphson,
Newton-Raphson ridge, quasi-Newton, quadratic, and trust-region
optimization methods. Perhaps the quadratic optimization method
is the optimization method that is sought. SAS OR has all these
optimization methods an more. (See PROC NLP documentation.)
In addition to PROC NLIN, the procedure NLMIXED offers a wide
variety of optimization techniques: virtually all of the
optimization techniques available in IML are available in the
procedure NLMIXED. In my opinion, the NLMIXED procedure has
the advantage of ease of specification of the likelihood function.
--- "Manolakos, Peter" <pmanolakos@ISO-NE.COM> wrote:
> I am not familiar with this optimization algorithm, of course, that
> fact does not imply it is not utilized by SAS. Consider PROC NLIN
> for instance. According to the SAS documentation there are four
> computational methods used to derive estimates (all of which are
> basically standard optimization algorithms): steepest descent,
> Gauss-Newton, Newton, Marquardt.
> Now, one could code easily the objective function in IML. And then
> you could code that method too.
> Perhaps if you provide the list with additional information someone
> could answer your question.
> -----Original Message-----
> From: Stuart Dennon [mailto:sdennon@HOTMAIL.COM]
> Sent: Thursday, May 01, 2003 10:20 PM
> To: SAS-L@LISTSERV.VT.EDU
> Subject: MLE regression with hill quadratic climbing
> The following question was asked by a member of the Australian
> SAS User Group - with no answer as yet.
> Dear All,
> Has anyone here ever used SAS to do the maximum likelihood estimation
> (by using the quadratic hill-climbing algorithm)?
> What SAS proc can be used?
> Any ideas or assistance would be kindly appreciated.
> Thanks in anticipation
> Stuart Dennon
Fred Hutchinson Cancer Research Center
Ph: (206) 667-2926
Fax: (206) 667-5977
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