New Local Estimation procedure for a Non-Parametric Regression Function for Longitudinal Data
纵向数据非参数回归函数的新局部估计方法
Summary The paper develops a new estimation of non-parametric regression functions for clustered or longitudinal data. We propose to use Cholesky decomposition and profile least squares techniques to estimate the correlation structure and regression function simultaneously. We further prove that the estimator proposed is as asymptotically efficient as if the covariance matrix were known. A Monte Carlo simulation study is conducted to examine the finite sample performance of…