Sparse group lasso for multiclass functional logistic regression models
Sparse group lasso for multiclass functional logistic regression models
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DOI：
10.1080/03610918.2018.1423693
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发表时间：
2019-07
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影响因子：
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通讯作者：
H. Matsui
中科院分区：
文献类型：
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作者：
H. Matsui
稀疏性诱导惩罚是变量选择的有用工具，对于数据是函数的回归问题也是有效的。利用稀疏正则化方法，研究了函数数据的多类逻辑回归模型中变量和决策边界的选择问题。在惩罚似然法框架下，利用稀疏组套索型惩罚对函数逻辑回归模型进行参数估计，然后根据模型选择准则选择模型的调优参数。通过仿真研究和基因表达数据集的分析，验证了该方法的有效性。
ABSTRACT Sparsity-inducing penalties are useful tools for variable selection and are also effective for regression problems where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in multiclass logistic regression models for functional data, using sparse regularization. The parameters of the functional logistic regression model are estimated in the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters for the model are selected using the model selection criterion. The effectiveness of the proposed method is investigated through simulation studies and the analysis of a gene expression data set.