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Document type:
Zeitschriftenaufsatz 
Author(s):
Min, A., Holzmann, H., and Czado, C. 
Title:
Model selection strategies for identifying most relevant covariates in homoscedastic linear models 
Abstract:
We propose a new method in two variations for the identification of most relevant covariates in linear models with homoscedastic errors. In contrast to AIC, BIC and other information criteria, our method is based on an interpretable scaled quantity. This quantity measures a maximal relative error one makes by selecting covariates from a given set of all available covariates. The proposed model selection procedures rely on asymptotic normality of test statistics, and therefore normality...    »
 
Journal title:
Computational Statistics and Data Analysis 
Year:
2010 
Journal volume:
54 
Pages contribution:
3194-3211 
Reviewed:
ja 
Language:
en 
Status:
Verlagsversion / published 
Semester:
SS 10 
Format:
Text