The Henderson Smoother in Reproducing Kernel Hilbert Space
用再生核希尔伯特空间方法为亨德森平滑器构建了三阶核表示，计算了两个密度函数和正交多项式，证明其在短中长度滤波中表现优异，且非对称滤波器在信号传递、噪声抑制和修正幅度上优于经典方法。
The Henderson smoother has been traditionally applied for trend-cycle estimation in the context of nonparametric \nseasonal adjustment software officially adopted by statistical agencies. This study introduces \na Henderson third-order kernel representation by means of the reproducing kernel Hilbert space (RKHS) \nmethodology. Two density functions and corresponding orthonormal polynomials have been calculated. \nBoth are shown to give excellent representations for short- and medium-length filters. Theoretical and \nempirical comparisons of the Henderson third-order kernel asymmetric filters are made with the classical \nones. The former are shown to be superior in terms of signal passing, noise suppression, and revision size.