Selecting the optimum k in ridge regression |
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Authors: | Tze-San Lee Don B. Campbell |
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Affiliation: | Department of Mathematics , Western Illinois University , |
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Abstract: | Two ridge rules are proposed for selecting the optimal k in ridge regression . Since the sampling distribution of the proposed rules are mathematically in tractable , a Monte Carlo study is conducted to examine their statisticl properties . Numerical results of the simulations in dicate that the performance of ridge rules depends upon the risk function used. Nevertheless, one of the ridge rules does produce a smaller mean squared error than the least squares estimator with the probability greater than 0.57 for all situations. |
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Keywords: | optimal ridge parameter mean squared estimation or prediction error Newton-Raphson method simulation |
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