RE: Confidence intervals of PsN bootstrap output

Leonid > of them. If some realizations are so special that the model behaves in > an unusual way (with any definition of unusual: non-convergence, not > convergence of the covariance step, parameter estimates at the boundary, > etc.) we either need to accept those as is, or work with each of those > special data sets one by one to push to the parameter estimates that we > can accept, or change the bootstrap procedure (add stratification by > covariates, by dose level, by route of administration, etc.) so that all > data sets behave similarly. I agree with your comments above and there are times that this might be possible. But in essence I hear that BS may become overtly time consuming in some cases if individual BS'd data sets require almost a full modelling procedure to gain appropriate convergence outcomes. This might make a long BS into an almost impossible task. The risk a user then runs is once you know there are runs that don't work as intended you cannot ignore them. If you can't afford the time resource (say a single run takes 12-24 hours) to recover each of these (say 20% of 1000 BS runs) and since you cannot un-know the results you're stuck. Would not a pragmatic view be appropriate? Report the outcomes of the BS runs as they occurred but then select those runs that terminated appropriately? Otherwise BS is too risky... Steve PS All models are wrong :-) -- > >