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March, 1993 Model Selection Via Multifold Cross Validation
Ping Zhang
Ann. Statist. 21(1): 299-313 (March, 1993). DOI: 10.1214/aos/1176349027

Abstract

A natural extension of the simple leave-one-out cross validation (CV) method is to allow the deletion of more than one observations. In this article, several notions of the multifold cross validation (MCV) method have been discussed. In the context of variable selection under a linear regression model, we show that the delete-d MCV criterion is asymptotically equivalent to the well known FPE criterion. Two computationally more feasible methods, the r-fold cross validation and the repeated learning-testing criterion, are also studied. The performance of these criteria are compared with the simple leave-one-out cross validation method. Simulation results are obtained to gain some understanding on the small sample properties of these methods.

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Ping Zhang. "Model Selection Via Multifold Cross Validation." Ann. Statist. 21 (1) 299 - 313, March, 1993. https://doi.org/10.1214/aos/1176349027

Information

Published: March, 1993
First available in Project Euclid: 12 April 2007

zbMATH: 0770.62053
MathSciNet: MR1212178
Digital Object Identifier: 10.1214/aos/1176349027

Subjects:
Primary: 62J05
Secondary: 62E20, 65C05

Rights: Copyright © 1993 Institute of Mathematical Statistics

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Vol.21 • No. 1 • March, 1993
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