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An optimal selection of regression variables

An optimal selection of regression variables 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. This content is only available as a PDF. © 1981 Biometrika Trust http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrika Oxford University Press

An optimal selection of regression variables

Biometrika , Volume 68 (1) – Apr 1, 1981

 
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References (18)

Publisher
Oxford University Press
Copyright
© 1981 Biometrika Trust
ISSN
0006-3444
eISSN
1464-3510
DOI
10.1093/biomet/68.1.45
Publisher site
See Article on Publisher Site

Abstract

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. This content is only available as a PDF. © 1981 Biometrika Trust

Journal

BiometrikaOxford University Press

Published: Apr 1, 1981

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