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Variable selection for sparse logistic regression
Metrika ( IF 0.7 ) Pub Date : 2020-02-06 , DOI: 10.1007/s00184-020-00764-4
Zanhua Yin

We consider the variable selection problem in a sparse logistical regression model. Inspired by the square-root Lasso, we develop a weighted score Lasso for logistical regression. The new method yields the estimation $${\ell }_1$$ ℓ 1 error bound under similar assumptions as introduced in Bach et al. (Electron J Stat 4:384–414, 2010). Compared to standard Lasso, the weighted score Lasso provides a direct choice for the tuning parameter. Both theoretical and simulation results confirm the satisfactory performance of the proposed method. We illustrate our methodology with a real microarray data set.

中文翻译:

稀疏逻辑回归的变量选择

我们考虑稀疏逻辑回归模型中的变量选择问题。受平方根套索的启发,我们开发了一个用于逻辑回归的加权分数套索。新方法在 Bach 等人引入的类似假设下产生估计 $${\ell }_1$$ ℓ 1 误差界限。(Electron J Stat 4:384–414, 2010)。与标准 Lasso 相比,加权分数 Lasso 为调整参数提供了直接选择。理论和仿真结果都证实了所提出方法的令人满意的性能。我们用一个真实的微阵列数据集来说明我们的方法。
更新日期:2020-02-06
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