st: Imputation vs. Reducing Dataset?

[John Simpson <john.simpson@ualberta.ca>]

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 remaining  
generations?).

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.

-John

John Simpson
Department of Philosophy
University of Alberta
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