st: Handling ologit convergence failure in bstrap

[Mike Lacy <Michael.Lacy@colostate.edu>]

I am doing some bootstrapping experiments using OLOGIT . I have 
deliberately chosen a skewed response variable (P = 0.1, 0.1, 0.1, 0.1, 
0.1, 0.6) and  6 covariates. The covariates are all scaled to the same 
order of magnitude. Perhaps not surprisingly, at relatively smaller sample 
sizes, (e.g., N = 100) , OLOGIT occasionally (say 1/100 reps) fails to 
converge at any reasonable tolerance setting, and halts BSTRAP. It would be 
nice if I could prevent this by tweaking the estimation process, but it 
seems that the tolerance is the only thing I can change on the maximization 
routine underlying OLOGIT. I presume but don't know that the routine is 
simply reacting to a really degengerate distribution on the response 
variable because leaving out some of the covariates doesn't seem to help. 
If anyone has ideas for promoting convergence, that would be good, but I'm 
not hopeful.

Presuming the convergence problem is intractable:
I would like to find a way for BSTRAP to continue,  simply ignore the 
occasional failure to converge, and  to accumulate the results from the 
reps that do converge.  Is there a  way this can be done?

Thanks,




=-=-=-=-=-=-=-=-=-=-=-=-=
Mike Lacy
Fort Collins CO
(970) 491-6721 office





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