Estimation in Covariance Components Models
本文开发了线性协方差分量模型的估计技术，重点解释计算过程，用贝叶斯方法处理已知方差协方差下的固定和随机效应估计，并通过EM算法从缺失数据中计算未知方差协方差的点估计，以法学院、田鼠和职业足球队数据为例。
Abstract Estimation techniques for linear covariance components models are developed and illustrated with special emphasis on explaining computational processes. The estimation of fixed and random effects when the variances and covariances are known is presented in Bayesian terms, Point estimates of the unknown variances and covariances are computed using the EM algorithm for maximum likelihood estimation from incomplete data. The techniques are illustrated with data on law schools, field mice, and professional football teams. Key Words: Covariance componentsLinear modelsMixed modelsRandom effectsMaximum likelihoodEM algorithm