BETTER SUBSET REGRESSION USING THE NONNEGATIVE GARROTE
BETTER SUBSET REGRESSION USING THE NONNEGATIVE GARROTE
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DOI：
10.2307/1269730
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发表时间：
1995-11-01
期刊：
影响因子：
2.5
通讯作者：
BREIMAN, L
中科院分区：
文献类型：
--
作者：
BREIMAN, L
提出了一种新的子集回归方法——非负绞喉法。它既收缩又使系数为零。在对真实和模拟数据的测试中，该方法的预测误差低于普通子集选择。它还与脊回归进行了比较。如果一个过程产生的回归方程不随着数据的微小变化而急剧变化，则该过程称为稳定的。子集选择是不稳定的，脊是非常稳定的，而nn-garrote是中间的。仿真结果说明了不稳定性对预测误差的影响。
A new method, called the nonnegative (nn) garrote, is proposed for doing subset regression. It both shrinks and zeroes coefficients. In tests on real and simulated data, it produces lower prediction error than ordinary subset selection. It is also compared to ridge regression. If the regression equations generated by a procedure do not change drastically with small changes in the data, the procedure is called stable. Subset selection is unstable, ridge is very stable, and the nn-garrote is intermediate. Simulation results illustrate the effects of instability on prediction error.