Improving Efficiency by Shrinkage: The James--Stein and Ridge Regression Estimators

By Marvin H. J. Gruber. 1998

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Kernel Mean Shrinkage Estimators (2014, background): “The James-Stein estimator is a special case of a larger class of estimators known as shrinkage estimators (Gruber, 1998).”
Using Liu-Type Estimator to Combat Collinearity (2003, methodology): “Ridge regression is recently reviewed in a book by Gruber (1998).”
Mean Squared Error Matrix Comparisons of Some Biased Estimators in Linear Regression (2003, methodology): “NUMERICAL EXAMPLE To illustrate our theoretical results in this last section we consider a dataset which was discussed in Gruber (1998).”
THE EFFICIENCY OF MODIFIED JACKKNIFE AND RIDGE TYPE REGRESSION ESTIMATORS: A COMPARISON (2008, unknown): “8) are all of the form (Gruber, [6]) γ̂i = [f(ki)]γ̂(iLS), (2.”
Model Evaluation by Comparison of Model-Based Predictions and Measured Values (2003, background): “…is the mean squared difference between model predictions sions are based on more data than just one individual datum (Gauch, 1993, 2003, p. 291–296; Gruber, 1998).and the true values, which can be estimated from available empirical data by means of proper calculations even But this second…”
The Restricted and Unrestricted Two-Parameter Estimators (2007, background): “However, it is not clear how this estimator is an improvement over the RR and the contraction estimators as Gruber (1998) mentioned.”