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Model averaging prediction for nonparametric varying-coefficient models with B-spline smoothing
Statistical Papers ( IF 1.3 ) Pub Date : 2021-01-15 , DOI: 10.1007/s00362-020-01218-9
Xiaochao Xia

Model averaging has been demonstrated as a powerful tool in statistical prediction over the past decade. However, a majority of related works focus on the parametric model averaging. In this paper, we propose a model averaging estimation under nonparametric varying-coefficient models. Differing from existing works, our proposal concentrates on the development of the B-spline approximation to nonparametric varying coefficient functions for model average estimator, rendering the computational burden more cheaply than the kernel smoothing based estimator. Furthermore, our procedure is asymptotically optimal under mild conditions. The asymptotic optimality established in current paper is in terms of conditional quadratic loss function when the variance of model error is known or unknown, respectively. Three different cases of candidate models are considered. Extensive simulations are carried out to evaluate the finite-sample performance of our estimator. A real data is analyzed for illustration as well.

中文翻译:

具有 B 样条平滑的非参数变系数模型的模型平均预测

在过去十年中,模型平均已被证明是统计预测中的强大工具。然而,大多数相关工作都集中在参数模型平均上。在本文中,我们提出了一种非参数变系数模型下的模型平均估计。与现有工作不同,我们的建议专注于开发模型平均估计器的非参数变系数函数的 B 样条近似,使计算负担比基于核平滑的估计器更便宜。此外,我们的程序在温和条件下是渐近最优的。当前论文中建立的渐近最优性分别是当模型误差的方差已知或未知时的条件二次损失函数。考虑了候选模型的三种不同情况。进行了广泛的模拟以评估我们估计器的有限样本性能。还分析了真实数据以进行说明。
更新日期:2021-01-15
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