纵向数据下变系数EV模型的光滑核估计

Kernel Smoothing Estimation for Varying Coefficient EV Models with Longitudinal Data

考虑纵向数据下变系数EV模型,提出了未知系数函数的局部纠偏光滑核估计,在适当条件下,证明了所提出的光滑核估计具有渐近正态性,由此构造回归系数的逐点置信区间.模拟研究了所提出方法的有限样本性质.

Varying coefficient EV models with longitudinal data are considered. The local bias-corrected kernel estimators for the unknown coefficient functions are proposed. It is shown that the proposed estimators are asymptotically normal under some suitable conditions, and hence it can be used to construct the pointwise confidence regions of the coefficient functions. The finite-sample properties of the proposed procedures are studied through a simulation study.