I recently did a Clusty search on “missing data”. The focus of the results turns out to be the statistical science of imputing missing data in experiments, etc.
Knowing about this is important for grounding our work and contrasting it with semantically data missing for reasons such as neuropsych or semantic assumptions. The generic statistical limits on outliers don’t really apply to what we are doing unless the context is bounded. By that I mean we may be in an open-world context, not a clinical measurement.
Missign Data: A Gentle Introduction
http://www.spss.com/missing_value/ http://www.statistics.com/courses/missing http://www.uvm.edu/~dhowell/StatPages/More_Stuff/Missing_Data/Missing.html http://www.fields.utoronto.ca/programs/scientific/04-05/missing-data/
Subject: Interesting missing value case: neuropsych evaluations
They’re trying to take neuropsych measurements on people with behavioral disorders due to dementia — the disorder causes them to be unable to gather the information.The actual lack of information (or inability to gather
it) is an indicator of the seriousness of the disorder they’re studying.