Title: Variable selection with incomplete covariate data
Authors: Claeskens, Gerda ×
Consentino, Fabrizio #
Issue Date: 2008
Publisher: Blackwell Publishers
Series Title: Biometrics vol:64 issue:4 pages:1062-1069
Abstract: Application of classical model selection methods such as
Akaike's information criterion AIC becomes problematic when observations
are missing. In this paper we propose some variations on the AIC,
which are applicable to missing covariate problems. The method
is directly based on the EM algorithm and is readily available
for EM-based estimation methods, without much additional computational
efforts. The missing data-AIC criteria are formally derived and
shown to work in a simulation study and by application to data on
diabetic retinopathy.
ISSN: 0006-341X
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

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