Title: Variable selection with incomplete covariate data
Authors: Claeskens, Gerda
Consentino, Fabrizio
Issue Date: 2007
Publisher: K.U.Leuven - Faculty of Economics and Applied Economics
Series Title: DTEW - KBI_0715 pages:1-21
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.
Publication status: published
KU Leuven publication type: IR
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven

Files in This Item:
File Description Status SizeFormat
KBI_0715.pdf Published 327KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.