Bounded Influence Estimation in the Mixed Linear Model
回顾了回归模型中的有界影响估计，并将其扩展到混合线性模型，通过加权最大似然和限制最大似然估计方程实现，给出了新估计量的渐近性质并应用于人工数据集和小规模模拟研究。
Bounded influence estimation (also known as generalized M or GM estimation) in the regression model is reviewed. The definitions of bounded influence estimation proposed by Mallows and Schweppe are then extended to the mixed linear model. This is achieved by applying appropriate weight functions to maximum likelihood and restricted maximum likelihood estimating equations. The asymptotic properties of the new estimators are obtained, and the estimators are applied to an artificial dataset. The article concludes with an extension of the example into a small simulation study designed to test some properties of the estimators in samples of moderate size. � 1997 Taylor & Francis Group, LLC.