re: st: Imputation vs. Reducing Dataset?

[David Airey <david.airey@Vanderbilt.Edu>]

.

Should you also consider a censored model, because you know your Xs  
but not the Y for those populations that got larger than your cutoff?

-Dave

> Hello Statarians,
> I have a very large set of data featuring population counts  
> generated by a computer simulation. In order to speed processing  
> populations that grew beyond 15000 within the 100 generation limit  
> were pulled from the simulation. As a result there are numerous  
> populations that now have missing data, making my panels unbalanced.
> I am curious how to best fit a model to this data given what is  
> missing. In particular, I have two worries:
> 1. That unless I do something the missing values will cause any  
> procedure to misrepresent the actual situation as the smaller values  
> that remain towards the end of the time period will skew the mean. I  
> am curious if this is a problem for populations that have died off  
> early as well (do I need to carry the 0 through all the  
> remaininggenerations?).
> 2. I am unsure whether imputation (with ice?) or chopping the  
> dataset or both is the best way to proceed. I know that ice needs  
> variables that are missing at random, but is there some way to  
> impute the missing values if I know how they are structured.
> Thank you.
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