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Selection Models and Pattern‐Mixture Models for Incomplete Data with Covariates
Author(s) -
Michiels Bart,
Molenberghs Geert,
Lipsitz Stuart R.
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00978.x
Subject(s) - categorical variable , covariate , missing data , selection (genetic algorithm) , computer science , model selection , focus (optics) , statistics , interval (graph theory) , data mining , econometrics , artificial intelligence , machine learning , mathematics , physics , optics , combinatorics
Summary. Most models for incomplete data are formulated within the selection model framework. This paper studies similarities and differences of modeling incomplete data within both selection and pattern‐mixture settings. The focus is on missing at random mechanisms and on categorical data. Point and interval estimation is discussed. A comparison of both approaches is done on side effects in a psychiatric study.