A Simulation Study on the Prediction Ability of Biased Regression Methods

This article examines three biased methods used for predictive modelling: ridge regression, principal component regression and partial least squares regression. A Monte Carlo study is performed to compare the prediction ability of these methods along with ordinary least squares. 48 different situations are considered and the root mean squared error of prediction is computed by using leave-one-out cross-validation for the comparison. Performance of the four methods are compared in detail.

1. Introduction

2. Background

3. Simulation Study

4. Results of the Simulation Study

5. Conclusions

References

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