Summary
The generalized ridge estimator, which considers generalizations of mean square error, is presented, and a mathematical rule of determining the optimalk-value is discussed. The generalized ridge estimator is examined in comparison with the least squares, the pseudoinverse, theJames-Stein-type shrinkage, and the principal component estimators, especially focusing their attention on improved adjustments for regression coefficients. An alternative estimation approach that better integrates a priori information is noted. Finally, combining the generalized ridge and robust regression methods is suggested.
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Myoken, H., Uchida, Y. The generalized ridge estimator and improved adjustments for regression parameters. Metrika 24, 113–124 (1977). https://doi.org/10.1007/BF01893398
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DOI: https://doi.org/10.1007/BF01893398