Talk:Missing completely at random

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I think this needs a rewrite

This article is in need of a more thorough treatment. It is missing formal mathematical definitions of the terms as well as standard implications of the definitions.

I'm going to remove the last sentence of the article for now. It is absolutely NOT advisable to be throwing out the items with incomplete responses in MNAR data prior to running an analysis. This produces asymptotically biased answers (see e.g. this book [1])! In general, you can only throw out missing data when it is MCAR, although this is a bad idea if you are a fan of being efficient in using your data. — Preceding unsigned comment added by 24.136.36.175 (talk) 19:01, 5 January 2012 (UTC)[reply]

I am not happy with the examples

The one for MAR (accidentally omitting an answer on a questionnaire) looks more like MCAR than MAR to me. The one for NMAR looks more like MAR! Shouldn't NMAR be an answer omitted because of the answer itself (like very depressed people skipping the depression item)? If it's missing due to some other (measured?) characteristics, then it seems like MAR.

I'm not a missing data expert, so I'm just throwing this out to the readership.

Ed Ed Gracely (talk) 17:01, 16 April 2013 (UTC)[reply]