Bounded Influence Estimation in the Mixed Linear Model
将回归模型中的有界影响估计（广义M估计）扩展到混合线性模型，通过给最大似然和限制最大似然估计方程加权重，得到新估计量的渐近性质，并用人工数据和中等样本模拟验证其表现。
Abstract 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.