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首页> 外文期刊>Communications in Statistics >M-Estimation for partially functional linear regression model based on splines
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M-Estimation for partially functional linear regression model based on splines

机译:基于样条曲线的部分函数线性回归模型的M估计

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摘要

M-estimation is a widely used technique for robust statistical inference. In this paper, we study robust partially functional linear regression model in which a scale response variable is explained by a function-valued variable and a finite number of real-valued variables. For the estimation of the regression parameters, which include the infinite dimensional function as well as the slope parameters for the real-valued variables, we use polynomial splines to approximate the slop parameter. The estimation procedure is easy to implement, and it is resistant to heavy-tailederrors or outliers in the response. The asymptotic properties of the proposed estimators are established. Finally, we assess the finite sample performance of the proposed method by Monte Carlo simulation studies.
机译:M估计是一种用于鲁棒统计推断的广泛使用的技术。在本文中,我们研究了稳健的部分函数线性回归模型,其中规模响应变量由函数值变量和有限数量的实值变量解释。为了估算回归参数,其中包括无穷维函数以及实值变量的斜率参数,我们使用多项式样条来近似斜率参数。估计程序易于实现,并且可以抵抗响应中的重尾错误或离群值。提出了估计量的渐近性质。最后,我们通过蒙特卡洛模拟研究评估了该方法的有限样本性能。

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