纵向数据广义部分线性模型的二次推断推断函数估计（英文）

Quadratic Inference Functions for Generalized Partially Linear Models with Longitudinal Data

针对纵向数据广义部分线性模型,通常的做法是用样条或核方法逼近非参部分,之后利用广义估计方程方法(GEE)估计参数部分.本文使用B样条逼近非参函数,并基于二次推断函数的方法对参数和非参数进行估计,并给出了估计量的大样本性质.模拟表明本文的方法改进了GEE的效率.

In this paper, the semiparametric generalized partially linear models （GPLMs） forlongitudinal data is studied. We approximate the nonparametric function in the GPLMs by a regression spline, and use quadratic inference functions （QIF） to take the within-cluster correlation into account without involving direct estimation of nuisance parameters in the correlation matrix. We establish the asymptotic normality of the resulting estimators. The finite sample performance of the proposed methods is evaluated through simulation studies and a real data analysis.