Partial and latent ignorability in missing-data problems
Partial and latent ignorability in missing-data problems
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DOI：
10.1093/biomet/asn069
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发表时间：
2009-03-01
期刊：
影响因子：
2.7
通讯作者：
Schafer, Joseph L.
中科院分区：
文献类型：
--
作者：
Harel, Ofer;Schafer, Joseph L.
When an assumption of missing at random is untenable, it becomes necessary to model missing-data indicators, which carry information about the parameters of the complete-data population. Within a given application, however, researchers may believe that some aspects of missingness are ignorable but others are not. We argue that there are two different ways to formalize the notion that only part of the missingness is ignorable. These approaches correspond to assumptions that we call partially missing at random and latently missing at random. We explain these concepts and apply them in a latent-class analysis of survey questions with item nonresponse.
