辑回归模型的Smooth LASSO及Spline LASSO变量选择
Variable Selection for Logistic Regression via Smooth LASSO and Spline LASSO
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摘要: 对于逻辑回归模型中的参数估计和变量选择问题,提出了Smooth LASSO以及Spline LASSO. 当变量具有连续性,使用Smooth LASSO, 可以获得局部恒定的系数. 但是在有些情况下,系数可能不同并且缓慢变化, 可以使用Spline LASSO来估计参数.本文通过理论证明模型的可靠性, 利用坐标下降法对模型进行求解,最后通过模拟验证了模型在变量选择中的准确性以及较好的预测性.Abstract: Considering a parameter estimation and variable selection problem in logistic regression, we propose Smooth LASSO and Spline LASSO. When the variables is continuous, using Smooth LASSO can select local constant coefficient in each group. However, in some case, the coefficient might be different and change smoothly. Using Spline Lasso to estimate parameter is more appropriate. In this article, we prove the reliability of the model by theory. Finally using coordinate descent algorithm to solve the model. Simulations show that the model works very effectively both in feature selection and prediction accuracy.