Asymptotic optimality of full cross-validation for selecting linear regression models

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.

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Year of publication:	1997
Authors:	Droge, Bernd
Publisher:	Berlin : Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Subject:	prediction \| model selection \| asymptotic optimality \| Cross-validation \| full cross-validation

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Series:	SFB 373 Discussion Paper ; 1997,5
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	728315890 [GVK] hdl:10419/66243 [Handle] RePEc:zbw:sfb373:19975 [RePEc]
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010310761