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Smoothing Spline ANOVA Decomposition of Arbitrary Splines: An Application to Eye Movements in Reading

机译:任意样条的平滑样条ANOVA分解:在阅读中眼睛运动中的应用

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摘要

The Smoothing Spline ANOVA (SS-ANOVA) requires a specialized construction of basis and penalty terms in order to incorporate prior knowledge about the data to be fitted. Typically, one resorts to the most general approach using tensor product splines. This implies severe constraints on the correlation structure, i.e. the assumption of isotropy of smoothness can not be incorporated in general. This may increase the variance of the spline fit, especially if only a relatively small set of observations are given. In this article, we propose an alternative method that allows to incorporate prior knowledge without the need to construct specialized bases and penalties, allowing the researcher to choose the spline basis and penalty according to the prior knowledge of the observations rather than choosing them according to the analysis to be done. The two approaches are compared with an artificial example and with analyses of fixation durations during reading.
机译:平滑样条方差分析(SS-ANOVA)需要基础和惩罚项的专门构造,以便合并有关要拟合数据的先验知识。通常,使用张量积样条求助于最通用的方法。这暗示着对相关结构的严格限制,即,一般不能合并平滑度各向同性的假设。这可能会增加样条曲线拟合的方差,尤其是在仅给出相对较小的一组观测值的情况下。在本文中,我们提出了一种替代方法,该方法无需构造专门的依据和惩罚就可以整合先验知识,从而使研究人员可以根据观测的先验知识选择样条基础和惩罚,而不是根据观测值来选择它们。分析要做。将这两种方法与一个人工示例进行比较,并与阅读期间的固定持续时间进行分析。

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