V. Oliker (1978)
On the relationship between the sample size and the number of variables in a linear regression modelCommunications in Statistics-theory and Methods, 7
R. Hocking (1976)
The analysis and selection of variables in linear regression
R. Newton, D. Spurrell (1967)
A Development of Multiple Regression for the Analysis of Routine DataApplied statistics, 16
H. Akaike (1978)
A Bayesian analysis of the minimum AIC procedureAnnals of the Institute of Statistical Mathematics, 30
H. Akaike (1973)
Information Theory and an Extension of the Maximum Likelihood Principle, 1
E. Hannan, B. Quinn (1979)
The determination of the order of an autoregressionJournal of the royal statistical society series b-methodological, 41
G. Schwarz (1978)
Estimating the Dimension of a ModelAnnals of Statistics, 6
E. Titchmarsh (1943)
The Laplace TransformNature, 151
C. Sims (1971)
Distributed Lag Estimation When the Parameter Space is Explicitly Infinite- DimensionalAnnals of Mathematical Statistics, 42
H. Akaike (1970)
Statistical predictor identificationAnnals of the Institute of Statistical Mathematics, 22
W. Feller (1959)
An Introduction to Probability Theory and Its Applications
M. Stone (1979)
Comments on Model Selection Criteria of Akaike and SchwarzJournal of the royal statistical society series b-methodological, 41
S. Park (1977)
Selection of Polynomial Terms for Response Surface ExperimentsBiometrics, 33
R. Shibata (1980)
Asymptotically Efficient Selection of the Order of the Model for Estimating Parameters of a Linear ProcessAnnals of Statistics, 8
N. Sugiura (1978)
Further analysts of the data by akaike' s information criterion and the finite correctionsCommunications in Statistics-theory and Methods, 7
H. Akaike (1974)
A new look at the statistical model identificationIEEE Transactions on Automatic Control, 19
D. Allen (1971)
Mean Square Error of Prediction as a Criterion for Selecting VariablesTechnometrics, 13
T. B., D. Widder (1943)
The Laplace TransformThe Mathematical Gazette, 27
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
Biometrika – Oxford University Press
Published: Apr 1, 1981
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.