10.18452/3791
Droge, Bernd
Asymptotic Optimality of Full Cross-validation for Selecting Linear Regression Models
Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
2006
prediction
model selection
asymptotic optimality
Cross-validation
full cross-validation
330 Wirtschaft
17 Wirtschaft
Humboldt-Universität zu Berlin
Humboldt-Universität zu Berlin
2017-06-15
2017-06-15
2006-05-16
2006-05-16
en
http://edoc.hu-berlin.de/series/sfb-373-papers/1997-5/PDF/5.pdf
http://edoc.hu-berlin.de/18452/4443
urn:nbn:de:kobv:11-10063605
For the problem of model selection, full cross-validation has been proposed as alternative criterion to the traditional cross-validation, particularly in cases where the latter one is not well defined. To justify the use of the new proposal we show that under some conditions, both criteria share the same asymptotic optimality property when selecting among linear regression models.