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Résumé:
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A common problem in multiple regression analysis is having to engage in a bias variance trade-off in order to maximize the performance of a model. A number of
methods have been developed to deal with this problem over the years with a variety of
strengths and weaknesses. Of these approaches the ridge estimator is one of the most
commonly used. This paper conducts an examination of the properties of the ridge
estimator and several alternatives in both deterministic and stochastic environments.
We find the ridge to be effective when the sample size is small relative to the number
of predictors. However, we also identify a few cases where some of the alternative
estimators can outperform the ridge estimator. Additionally, we provide examples of
applications where these cases may be relevant. |
