Modern research data, where a large number of functional predictors is collected on few subjects are becoming increasingly common. In this paper we propose a variable selection technique, when the predictors are functional and the response is scalar. Our approach is based on adopting a generalized functional linear model framework and using a penalized likelihood method that simultaneously controls the sparsity of the model and the smoothness of the corresponding coefficient functions by adequate penalization. The methodology is characterized by high predictive accuracy, and yields interpretable models, while retaining computational efficiency. The proposed method is investigated numerically in finite samples, and applied to a diffusion tensor imaging tractography data set and a chemometric data set. Copyright © 2013 John Wiley & Sons Ltd

中文翻译：

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广义函数线性模型中的变量选择。

现代研究数据，即在少数主题上收集大量功能预测变量，变得越来越普遍。在本文中，当预测变量有效并且响应是标量时，我们提出了一种变量选择技术。我们的方法基于采用广义函数线性模型框架并使用惩罚似然方法，通过适当的惩罚同时控制模型的稀疏性和相应系数函数的平滑度。该方法的特点是预测精度高，并产生可解释的模型，同时保持计算效率。所提出的方法在有限样本中进行数值研究，并应用于扩散张量成像纤维束成像数据集和化学计量数据集。版权所有 © 2013 约翰·威利父子有限公司