Abstract:
:In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the focused information criterion (FIC) for variable selection in logistic regression. The FIC gives, depending on the quantity to be estimated, possibly different sets of selected variables. The standard version of the FIC measures the mean squared error of the estimator of the quantity of interest in the selected model. In this article, we propose more general versions of the FIC, allowing other risk measures such as the one based on L(p) error. When prediction of an event is important, as is often the case in medical applications, we construct an FIC using the error rate as a natural risk measure. The advantages of using an information criterion which depends on both the quantity of interest and the selected risk measure are illustrated by means of a simulation study and application to a study on diabetic retinopathy.
journal_name
Biometricsjournal_title
Biometricsauthors
Claeskens G,Croux C,Van Kerckhoven Jdoi
10.1111/j.1541-0420.2006.00567.xsubject
Has Abstractpub_date
2006-12-01 00:00:00pages
972-9issue
4eissn
0006-341Xissn
1541-0420pii
BIOM567journal_volume
62pub_type
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