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Monotone missing data and pattern‐mixture models
Author(s) -
Molenberghs G.,
Michiels B.,
Kenward M. G.,
Diggle P. J.
Publication year - 1998
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/1467-9574.00075
Subject(s) - missing data , mixture model , monotone polygon , computer science , model selection , selection (genetic algorithm) , mathematics , imputation (statistics) , data mining , artificial intelligence , pattern recognition (psychology) , statistics , geometry
It is shown that the classical taxonomy of missing data models, namely missing completely at random, missing at random and informative missingness, which has been developed almost exclusively within a selection modelling framework, can also be applied to pattern‐mixture models. In particular, intuitively appealing identifying restrictions are proposed for a pattern‐mixture MAR mechanism.