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In statistical prediction, classical approaches for model selection and model evaluation based on covariance penalties are still widely used.
Multiple regression
Charles Stein · 1960
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John W. Tukey · 1967
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Theodore Groves and Thomas Rothenberg · 1969
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On criteria for choosing a regression equation for prediction
Stanley L. Sclove · 1969
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Information theory and an extension of the maximum likelihood principle
Hirotogu Akaike · 1973
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Colin Mallows · 1973
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The analysis and selection of variables in linear regression
Ronald R. Hocking · 1976
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Generalized cross-validation as a method for choosing a good ridge parameter
Gene H Golub, Michael Heath, and Grace Wahba · 1979
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Charles Stein · 1981
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Leo Breiman and David Freedman · 1983
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How biased is the apparent error rate of a prediction rule?
Bradley Efron · 1986
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Grace Wahba · 1990
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Leo Breiman and Philip Spector · 1992
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Laszlo Gyorfi, Michael Kohler, Adam Krzyzak, and Harro Walk · 2002
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The Elements of Statistical Learning; Data Mining, Inference and Prediction
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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A survey of cross-validation procedures for model selection
Sylvain Arlot and Alain Celisse · 2010
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Ryan J. Tibshirani and Jonathan Taylor · 2012
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Sourav Chatterjee · 2013
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Optimal equivariant prediction for high-dimensional linear models with arbitrary predictor covariance
Lee H. Dicker · 2013
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The lasso problem and uniqueness
Ryan J. Tibshirani · 2013
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Selection of variables in multiple regression: Part I. A review and evaluation
Mary L. Thompson
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Models as approximations, Part I: A conspiracy of nonlinearity and random regressors in linear regression
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Models as approximations, Part II: A general theory of model-robust regression
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High-Dimensional Statistics: A Non-Asymptotic View
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