估计员
数学
二次方程
推论
应用数学
估计方程
功能(生物学)
二次函数
稳健性(进化)
M-估计量
统计
计算机科学
人工智能
生物化学
化学
几何学
进化生物学
基因
生物
作者
Samuel Müller,Suojin Wang,A. H. Welsh
摘要
Abstract We present a detailed discussion of the theoretical properties of quadratic inference function estimators of the parameters in marginal linear regression models. We consider the effect of the choice of working correlation on fundamental questions including the existence of quadratic inference function estimators, their relationship with generalized estimating equations estimators, and the robustness and asymptotic relative efficiency of quadratic inference function and generalized estimating equations estimators. We show that the quadratic inference function estimators do not always exist and propose a way to handle this. We then show that they have unbounded influence functions and can be more or less asymptotically efficient than generalized estimating equations estimators. We also present empirical evidence to demonstrate these results. We conclude that the choice of working correlation can have surprisingly large effects.
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