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Dokumenttyp:
Zeitschriftenaufsatz 
Autor(en):
Min, A.; Holzmann, H.; Czado, C. 
Nicht-TUM Koautoren:
nein 
Kooperation:
Titel:
Model selection strategies for identifying relevant covariates in homescedastic linear models 
Abstract:
A new method in two variations for the identification of most relevant covariates in linear models with homoscedastic errors is proposed. In contrast to many known selection criteria, the 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 of the errors in t...    »
 
Intellectual Contribution:
Discipline-based Research 
Zeitschriftentitel:
Computational Statistics and Data Analysis 
Jahr:
2010 
Band / Volume:
54 
Heft / Issue:
12 
Seitenangaben Beitrag:
3194-3211 
Reviewed:
ja 
Sprache:
en 
Status:
Erstveröffentlichung 
Format:
Text 
Key publication:
Nein 
Peer reviewed:
Ja 
International:
Ja 
Book review:
Nein 
commissioned:
not commissioned 
Professional Journal:
Nein