通过双层选择为函数数据选择变量和决策边界
Selection of variables and decision boundaries for functional data via bi-level selection
摘要：
稀疏性诱导惩罚是变量选择的有用工具，对于数据为函数的回归设置也很有效。我们考虑的问题，不仅选择变量，而且决策边界的逻辑斯谛回归模型的功能数据，使用稀疏正则化。在惩罚似然法的框架下，利用稀疏群套索型惩罚对函数型逻辑斯谛回归模型进行估计，然后利用模型选择准则选择调整参数.通过对实际数据的分析，验证了该方法的有效性.
英文摘要：
Sparsity-inducing penalties are useful tools for variable selection and they are also effective for regression settings where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in logistic regression models for functional data, using the sparse regularization. The functional logistic regression model is estimated by the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters are selected using the model selection criterion. The effectiveness of the proposed method is investigated through real data analysis.
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