This paper discusses childhood growth curve fitting by nonparametric models, and provides two methods for nonparametric curve fitting: smoothing spline and kernel estimator.
本文讨论了用非参数模型对儿童生长曲线进行拟合，给出了两种非参数曲线拟合方法：光滑样条和核估计。
The result also reveals that B-spline model performs better than other models integralively in precision of fitting prices, curve smoothness and stability.
样条法在利率期限结构的拟合精度、曲线光滑性及平稳性方面的综合效果最好。
Results of the test using field data show that the spline fitting can make a better compromise of best fitting and smoothness, and the algorithm performs better in short-term traffic flow forecasting.
经过实测数据仿真试验表明，样条拟合能较好地兼顾最优拟合与曲线光滑度的选择，算法的预测效果良好。
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