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An additive penalty P-Spline approach to derivative estimation

机译:累加罚分P样条法进行导数估计

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摘要

P-Splines are commonly used for derivative estimation where a non-linear relationship exists between the response and explanatory variables. However, questions about the error of these estimates have arisen. Incorporating an extra penalty term in a P-Spline model is proposed as an improvement when derivative estimation is of primary concern. This additive penalty approach to derivative estimation is shown to improve on the P-Spline estimates based on the results of a simulation study to compare the performance when estimating the first and second derivatives of six simulated functions. A method for generating variability bands for P-Spline derivative estimates with and without an additive penalty is given. The proposed additive penalty variability bands are shown to behave better than their single penalty counterpart. Motivating examples in environmental and sports science are used to demonstrate the need for accurate derivative estimates and the benefit of using an additional penalty term to this end.
机译:P样条通常用于微分估计,其中响应和解释变量之间存在非线性关系。但是,出现了有关这些估计的误差的问题。当主要考虑导数估计时,建议在P样条模型中加入额外的惩罚项作为改进。这种基于加法罚分法的导数估计方法在基于仿真研究结果的P样条估计值上得到了改进,该方法可以比较估计六个仿真函数的一阶和二阶导数时的性能。给出了一种生成带有和不带有加法罚分的P样条导数估计的变异带的方法。拟议的加性罚分可变性带表现出比其单罚性罚分更好的表现。使用环境和体育科学中的激励示例来证明需要精确的导数估计,以及为此目的使用附加惩罚项的好处。

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