Classification of Non-Parametric Regression Functions in Longitudinal Data Models
研究纵向数据中个体回归函数可能不同的情况，提出一种统计方法从数据中估计未知的组结构，并给出渐近性质和模拟验证。
Summary We investigate a longitudinal data model with non-parametric regression functions that may vary across the observed individuals. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the data. Moreover, we derive the asymptotic properties of the procedure and investigate its finite sample performance by means of a simulation study and a real data example.