论文标题
异步纵向数据的广义变化系数模型的局部稀疏估计器
Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data
论文作者
论文摘要
在纵向研究中，常见的是，反应和协变量不是同时测量的，这在很大程度上使分析变得复杂。在本文中，我们考虑了具有异步观测的广义变化系数模型的估计。在功能数据分析的框架中，通过内核技术构建了受惩罚的内核加权方程式。此外，在估计方程中还考虑了局部稀疏性，以提高估计值的解释性。我们在计算中扩展了迭代重新加权的最小二乘（IRL）算法。与现有方法相比，根据一致性和稀疏性，建立了理论特性，并且模拟研究进一步验证了我们方法的令人满意的性能。该方法应用于艾滋病研究以揭示其实际优点。
In longitudinal study, it is common that response and covariate are not measured at the same time, which complicates the analysis to a large extent. In this paper, we take into account the estimation of generalized varying coefficient model with such asynchronous observations. A penalized kernel-weighted estimating equation is constructed through kernel technique in the framework of functional data analysis. Moreover, local sparsity is also considered in the estimating equation to improve the interpretability of the estimate. We extend the iteratively reweighted least squares (IRLS) algorithm in our computation. The theoretical properties are established in terms of both consistency and sparsistency, and the simulation studies further verify the satisfying performance of our method when compared with existing approaches. The method is applied to an AIDS study to reveal its practical merits.
