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Pseudo‐Likelihood for Combined Selection and Pattern‐Mixture Models for Incomplete Data
Author(s) -
Molenberghs Geert,
Michiels Bart,
Kenward Michael G.
Publication year - 1998
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/(sici)1521-4036(199809)40:5<557::aid-bimj557>3.0.co;2-s
Subject(s) - covariate , bivariate analysis , model selection , dropout (neural networks) , computer science , statistics , econometrics , mathematics , machine learning
In this paper we develop pseudo‐likelihood methods for the estimation of parameters in a model that is specified in terms of both selection modelling and pattern‐mixture modelling quantities. Two cases are considered: (1) the model is specified directly from a joint model for the measurement and dropout processes; (2) conditional models for the measurement process given dropout and vice versa are specified directly. In the latter case, compatibility constraints to ensure the existence of a joint density are derived. The method is applied to data from a psychiatric study, where a bivariate therapeutic outcome is supplemented with covariate information.