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- W2029082950 abstract "Abstract A mean squared error criterion is used to compare five estimators of the coefficients in a linear regression model: least squares, principal components, ridge regression, latent root, and a shrunken estimator. Each of the biased estimators is shown to offer improvement in mean squared error over least squares for a wide range of choices of the parameters of the model. The results of a simulation involving all five estimators indicate that the principal components and latent root estimators perform best overall, but the ridge regression estimator has the potential of a smaller mean squared error than either of these." @default.
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- W2029082950 date "1977-09-01" @default.
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- W2029082950 title "Biased Estimation in Regression: An Evaluation Using Mean Squared Error" @default.
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- W2029082950 doi "https://doi.org/10.1080/01621459.1977.10480625" @default.
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