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st: Handling ologit convergence failure in bstrap

From   Mike Lacy <>
Subject   st: Handling ologit convergence failure in bstrap
Date   Wed, 18 Jun 2003 17:02:09 -0600

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?


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

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