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。

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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。

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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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