论文标题
可用于多元空间平滑的稳健薄板花键
Robust thin-plate splines for multivariate spatial smoothing
论文作者
论文摘要
我们提出了一个新型的基于薄板（Sobolev）惩罚的多元鲁棒性Smoothers家族，该家族特别适合分析空间数据。即使在高维度中，也可以在坐标轴的刚性转换方面不变，可以证明提出的估计量家族是不变的，并且在轻度假设下具有最佳的理论特性。在仿真研究中说明了所提出的薄板样条估计量相对于其非持射击的竞争性能，以及涉及有关臭氧浓度的二维地理数据的真实数据示例。
We propose a novel family of multivariate robust smoothers based on the thin-plate (Sobolev) penalty that is particularly suitable for the analysis of spatial data. The proposed family of estimators can be expediently computed even in high dimensions, is invariant with respect to rigid transformations of the coordinate axes and can be shown to possess optimal theoretical properties under mild assumptions. The competitive performance of the proposed thin-plate spline estimators relative to its non-robust counterpart is illustrated in a simulation study and a real data example involving two-dimensional geographical data on ozone concentration.
