Self-modelling warping functions
Self-modelling warping functions
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DOI：
10.1111/j.1467-9868.2004.b5582.x
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发表时间：
2004-01-01
影响因子：
5.8
通讯作者：
Gasser, T
中科院分区：
文献类型：
--
作者：
Gervini, D;Gasser, T
本文介绍了函数数据的半参数模型。假设翘曲函数是q个共同分量的线性组合，这些分量是从数据中估计出来的（因此称为“自建模”）。即使很小的q值也能提供与非参数方法相当的显著的模型灵活性。同时，这种方法避免了过拟合，因为共同成分是结合个体间的数据来估计的。作为一个方便的副产品，成分分数通常是可解释的，可以用于统计推断（给出了一个基于分数的分类示例）。
The paper introduces a semiparametric model for functional data. The warping functions are assumed to be linear combinations of q common components, which are estimated from the data (hence the name 'self-modelling'). Even small values of q provide remarkable model flexibility, comparable with nonparametric methods. At the same time, this approach avoids overfitting because the common components are estimated combining data across individuals. As a convenient by-product, component scores are often interpretable and can be used for statistical inference (an example of classification based on scores is given).