Variable selection in functional additive regression models
Variable selection in functional additive regression models
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DOI：
10.1007/s00180-018-0844-5
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发表时间：
2019-06-01
影响因子：
1.3
通讯作者：
Oviedo de la Fuente, Manuel
中科院分区：
文献类型：
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作者：
Febrero-Bande, Manuel;Gonzalez-Manteiga, Wenceslao;Oviedo de la Fuente, Manuel
本文考虑函数变量可能与其他类型的变量(标量、多变量、方向性等)混合的回归模型中的变量选择问题。我们的建议从一个简单的零模型开始，并基于Szekely等人提出的使用距离相关性(Ann Stat 35(6)：2769-2794,2007)，顺序地选择一个新变量并入该模型中。为简单起见，本文仅使用加性模型。然而，所提出的算法可以评估贡献的类型(线性、非线性、...)每个变量的。该算法在模拟和真实数据集上的应用显示了非常有希望的结果。
This paper considers the problem of variable selection in regression models in the case of functional variables that may be mixed with other type of variables (scalar, multivariate, directional, etc.). Our proposal begins with a simple null model and sequentially selects a new variable to be incorporated into the model based on the use of distance correlation proposed by Szekely et al.(Ann Stat 35(6):2769-2794, 2007). For the sake of simplicity, this paper only uses additive models. However, the proposed algorithm may assess the type of contribution (linear, non linear, ...) of each variable. The algorithm has shown quite promising results when applied to simulations and real data sets.