Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/354
Title: Selection models and pattern-mixture models for incomplete categorical data with covariates
Authors: MICHIELS, Bart 
MOLENBERGHS, Geert 
Lipsitz, Stuart R.
Issue Date: 1999
Publisher: INTERNATIONAL BIOMETRIC SOC
Source: Biometrics, 55(3). p. 978-983
Abstract: 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.
Keywords: categorical data; maximum likelihood estimation; missing data; multiple imputation; sensitivity analysis
Document URI: http://hdl.handle.net/1942/354
ISSN: 0006-341X
e-ISSN: 1541-0420
DOI: 10.1111/j.0006-341X.1999.00978.x
ISI #: 000082683000047
Type: Journal Contribution
Validations: ecoom 2000
Appears in Collections:Research publications

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