局部线性回归方法及其稳健形式已经被看作一种有效的非参数光滑方法.与流行的核回归方法相比,它有诸多优点,诸如:较高的渐近效率和较强的适应设计能力.另外,局部线性回归能适应几乎所有的回归设计情形却不需要任何边界修正。,Local linear regression methods and their solid form has been seen as an effective non-parametric smoothing method. Contrary to popular kernel

这个帖子中我想讨论的是移动窗口多项式最小二乘拟和平滑方法，粗糙惩罚方法，以及kernel平滑方法。-Posts in this discussion I think are moving window least squares polynomial fitting smoothing method, crude methods of punishment, as well as the kernel smoothing method.

We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutu

用5种非参数方法：线性样条、B样条、N-W核估计、最近邻估计、局部多项式对某地SO2浓度数据进行拟合。(5 non parametric methods: linear spline, B spline, N-W kernel estimation, nearest neighbor estimation, and local polynomial are used to fit the SO2 concentration data in a certain area.)