Variable selection for logistic regression using a prediction-focused information criterion.

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

Biometrics

journal_title

Biometrics

authors

Claeskens G,Croux C,Van Kerckhoven J

doi

10.1111/j.1541-0420.2006.00567.x

subject

Has Abstract

pub_date

2006-12-01 00:00:00

pages

972-9

issue

4

eissn

0006-341X

issn

1541-0420

pii

BIOM567

journal_volume

62

pub_type

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