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re: st: Imputation vs. Reducing Dataset?


From   David Airey <david.airey@Vanderbilt.Edu>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   re: st: Imputation vs. Reducing Dataset?
Date   Mon, 13 Jul 2009 14:34:01 -0500

.

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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