Date of Award
Doctor of Philosophy (PhD)
R. Carter Hill
The dissertation addresses three issues in the use of Stein-like estimators of the classical normal linear regression model. The St. Louis equation is used to generate out-of-sample forecasts using least squares. These forecasts are compared to those produced by restricted least squares, pretest, and members of a general family of minimax shrinkage estimators using the root-mean-square error criterion. Bootstrap confidence intervals and ellipsoids are constructed which are centered at least squares and James-Stein estimators and their coverage probability and size is explored in a Monte Carlo experiment. A Stein-like estimator of the probit regression model is suggested and its quadratic risk properties are explored in a Monte Carlo experiment.
Adkins, Lee Chester, "Stein-Like Estimation and Inference." (1988). LSU Historical Dissertations and Theses. 4552.