论文标题
模型检测和模式变化系数模型的可变选择
Model detection and variable selection for mode varying coefficient model
论文作者
论文摘要
不同系数模型通常用于统计建模，因为它比参数模型更灵活。但是，在模式回归中，模型检测和变化系数模型的可变选择知之甚少。文献中有关这些问题的现有方法通常基于平均回归和分位回归。在本文中，我们提出了一种新的方法，可以根据B-Spline近似和SCAD惩罚来解决这些问题的这些问题，以改变系数模型。此外，我们提出了一种新算法来估计感兴趣的参数，并讨论调整参数和带宽的参数选择。我们还建立了在某些规则条件下估计系数的渐近性能。最后，我们通过一些模拟研究和一个经验示例来说明所提出的方法。
Varying coefficient model is often used in statistical modeling since it is more flexible than the parametric model. However, model detection and variable selection of varying coefficient model are poorly understood in mode regression. Existing methods in the literature for these problems often based on mean regression and quantile regression. In this paper, we propose a novel method to solve these problems for mode varying coefficient model based on the B-spline approximation and SCAD penalty. Moreover, we present a new algorithm to estimate the parameters of interest, and discuss the parameters selection for the tuning parameters and bandwidth. We also establish the asymptotic properties of estimated coefficients under some regular conditions. Finally, we illustrate the proposed method by some simulation studies and an empirical example.
