Abstract
An asymptotically optimal selection of regression variables is proposed. The key assumption is that the number of control variables is infinite or increases with the sample size. It is also shown that Mallows's Cp', Akaike's FPE and aic methods are all asymptotically equivalent to this method.
Keywords
Akaike information criterionMathematicsStatisticsAsymptotically optimal algorithmSelection (genetic algorithm)RegressionRegression analysisSample size determinationApplied mathematicsEconometricsMathematical optimizationArtificial intelligenceComputer science
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Publication Info
- Year
- 1981
- Type
- article
- Volume
- 68
- Issue
- 1
- Pages
- 45-54
- Citations
- 537
- Access
- Closed
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Cite This
Ritei Shibata
(1981).
An optimal selection of regression variables.
Biometrika
, 68
(1)
, 45-54.
https://doi.org/10.1093/biomet/68.1.45
Identifiers
- DOI
- 10.1093/biomet/68.1.45