Robust Estimation of the Mean and Covariance Matrix from Data with Missing Values
将Rubin（1983）的稳健估计方法扩展到含缺失值的数据，使用多元t分布和污染正态模型通过EM算法进行最大似然估计，模拟和实例表明优于现有方法。
SUMMARY Methods of Rubin (1983) for robust estimation of a mean and covariance matrix and associated parameters are extended to analyse data with missing values. The methods are maximum likelihood (ML) for multivariate t and contaminated normal models. ML estimation is achieved by the EM algorithm, and involves minor modifications to the EM algorithm for multivariate normal data. The methods are shown to be superior to existing methods in a simulation study, using data generated from a variety of models. Model selection and standard error estimation are discussed with the aid of two real data examples.