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

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  • Droge, Bernd

Abstract

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.

Suggested Citation

  • Droge, Bernd, 1997. "Asymptotic optimality of full cross-validation for selecting linear regression models," SFB 373 Discussion Papers 1997,5, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:19975
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    1. Bunke, O. & Droge, B. & Polzehl, J., 1995. "Model Selection, Transformations and Variance Estimation in Nonlinear Regression," SFB 373 Discussion Papers 1995,52, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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    1. Droge, Bernd, 1999. "Asymptotic optimality of full cross-validation for selecting linear regression models," Statistics & Probability Letters, Elsevier, vol. 44(4), pages 351-357, October.

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