Asymptotic optimality of full cross-validation for selecting linear regression models - Bernd Droge (Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, 1997)
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.
urn:nbn:de:kobv:11-10063605
Keywords: 330; prediction; model selection; asymptotic optimality; Cross-validation; full cross-validation