Optimal Prediction in Linear Regression Analysis
نویسنده
چکیده
Expressions are derived for generalized ridge and ordinary ridge predictors that are optimal in terms of mean squared error of prediction (MSEP) for predicting the response at a single or at multiple future observation(s). Using the MSEP criterion, operational predictors are compared to the ordinary least squares (OLS) predictor and to several biased predictors derived from some popular biased estimators. Simulation results indicate that the performance of these predictors depends on the direction of the prediction, the magnitude of the signal-to-noise ratio, the level of multicollinearity, and the number of explanatory variables.
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