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Original Articles

The Average Estimated Variance Criterion for the Selection-of-Variables Problem in General Linear Models

Pages 261-273 | Published online: 09 Apr 2012

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BONG-JIN YUM$suffix/text()$suffix/text() & JAI-HYUN BYUN. (1990) Analysis of the Prediction Problem with Errors in the Variables. IIE Transactions 22:1, pages 73-83.
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Jorma Rissanen. (1987) Stochastic complexity and the mdl principle. Econometric Reviews 6:1, pages 85-102.
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Jacqueline S. Galpin & Douglas M. Hawkins. (1986) Variable subset selection for optimal regression prediction at a specified point. Journal of Applied Statistics 13:2, pages 187-198.
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Bong Jin Yum & JohnB. Neuhardt. (1984) Analysis of the Prediction Problem in a Simple Functional Relationship Model. IIE Transactions 16:2, pages 177-184.
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R.R. Hocking. (1983) Developments in Linear Regression Methodology: 1959–l982. Technometrics 25:3, pages 219-230.
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A.s. Young. (1982) The Bivar Criterion for Selecting Regressors. Technometrics 24:3, pages 181-189.
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RichardF. Gunst & RobertL. Mason. (1979) Some Considerations in the Evaluation of Alternate Prediction Equations. Technometrics 21:1, pages 55-63.
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M. Stone. (1978) Cross-validation:a review . Series Statistics 9:1, pages 127-139.
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Articles from other publishers (6)

E.A. Averbuch. 1992. Fault Detection, Supervision and Safety for Technical Processes 1991. Fault Detection, Supervision and Safety for Technical Processes 1991 179 183 .
E.A. Averbuch. (1991) Detecting Changes in Statistical Models with Application to Well-Logging Signal Processing. IFAC Proceedings Volumes 24:6, pages 179-183.
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A. S. Young. (1987) On a bayesian criterion for choosing predictive sub-models in linear regression. Metrika 34:1, pages 325-339.
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Jorma Rissanen. (1987) Stochastic Complexity. Journal of the Royal Statistical Society Series B: Statistical Methodology 49:3, pages 223-239.
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Tie-Jun Shan. (1987) On predictive least squares filtering. On predictive least squares filtering.
L. ANDERSON. (1985) Robust parameter identification for nonlinear systems using a principal components regression algorithm. Robust parameter identification for nonlinear systems using a principal components regression algorithm.

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