高采文, 甘华来. 增长曲线模型的非参数估计[J]. 应用概率统计, 2013, 29(6): 655-665.
引用本文: 高采文, 甘华来. 增长曲线模型的非参数估计[J]. 应用概率统计, 2013, 29(6): 655-665.
Gao Caiwen, Gan Hualai. Nonparametric Regression Method for Growth Curve Model[J]. Chinese Journal of Applied Probability and Statistics, 2013, 29(6): 655-665.
Citation: Gao Caiwen, Gan Hualai. Nonparametric Regression Method for Growth Curve Model[J]. Chinese Journal of Applied Probability and Statistics, 2013, 29(6): 655-665.

增长曲线模型的非参数估计

Nonparametric Regression Method for Growth Curve Model

  • 摘要: 增长曲线在研究中通常假定为时间的多项式形式, 大多数研究者都是通过选取高阶多项式的方式来提高估计的精度. 但这种方法存在很多缺陷, 如模型易受异常点的影响, 多项式假设要求过高等. 本文首次将局部多项式这种非参数估计方法应用到增长曲线模型中, 提出了非参数增长曲线模型, 给出了它的局部多项式估计, 并讨论了估计的渐近性质和理论带宽的选择. 最后对参数估计和非参数估计进行了模拟比较, 从拟合图和平均均方误差箱形图得到的结论是非参数估计效果较好.

     

    Abstract: In the research it is frequently assumed that the growth curve is a polynomial in time. In practice, researchers mainly use higher-order polynomials to obtain more precise estimates. But this method has many defects, such as the model can be easily affected by outliers and the polynomial hypothesis may be much strong in practice. So in this paper we first proposed nonparametric approach, local polynomial, instead of parametric method for estimation in growth curve model. We give the nonparametric growth curve model, and its nonparametric estimation. Then discuss the large sample character of local polynomial estimate. The ideal theoretical choice of a local bandwidth is also discussed in detail in this paper. Finally, through the simulation study, from the fitting curve and average square error box plot we can clearly see that the performance of nonparametric approach is much better than parametric technique.

     

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