论文标题
功能性变化的模型在异性疾病下，并应用于DTI数据
Functional varying-coefficient model under heteroskedasticity with application to DTI data
论文作者
论文摘要
在本文中，我们开发了一个多步估计程序，以基于连续的力矩条件的局部线性通用方法（GMM）同时估计函数。为了结合空间依赖性，首先将连续的力矩条件投射到本征函数上，然后通过加权本本值组合，从而解决了直接使用逆协方差操作员的挑战。我们提出了一个最佳仪器变量，该变量可以最大程度地减少所有局部线性GMM估计器类别之间的渐近方差函数，并且它的表现优于不包含空间依赖性的初始估计值。我们提出的方法显着提高了杂质性及其渐近特性下估计的准确性。大量的仿真研究说明了有限的样本性能，并且通过实际数据分析证实了所提出方法的功效。
In this paper, we develop a multi-step estimation procedure to simultaneously estimate the varying-coefficient functions using a local-linear generalized method of moments (GMM) based on continuous moment conditions. To incorporate spatial dependence, the continuous moment conditions are first projected onto eigen-functions and then combined by weighted eigen-values, thereby, solving the challenges of using an inverse covariance operator directly. We propose an optimal instrument variable that minimizes the asymptotic variance function among the class of all local-linear GMM estimators, and it outperforms the initial estimates which do not incorporate the spatial dependence. Our proposed method significantly improves the accuracy of the estimation under heteroskedasticity and its asymptotic properties have been investigated. Extensive simulation studies illustrate the finite sample performance, and the efficacy of the proposed method is confirmed by real data analysis.
