Carriquiry A L, Gianola D, Fernando R L
Department of Animal Sciences, University of Illinois, Urban 61801.
Biometrics. 1987 Dec;43(4):929-39.
A mixed-model procedure for analysis of censored data assuming a multivariate normal distribution is described. A Bayesian framework is adopted which allows for estimation of fixed effects and variance components and prediction of random effects when records are left-censored. The procedure can be extended to right- and two-tailed censoring. The model employed is a generalized linear model, and the estimation equations resemble those arising in analysis of multivariate normal or categorical data with threshold models. Estimates of variance components are obtained using expressions similar to those employed in the EM algorithm for restricted maximum likelihood (REML) estimation under normality.
描述了一种用于分析删失数据的混合模型方法，该方法假定数据服从多元正态分布。采用了贝叶斯框架，在记录左删失时，该框架允许估计固定效应和方差分量，并预测随机效应。该方法可扩展到右删失和双侧删失。所采用的模型是广义线性模型，估计方程类似于在使用阈值模型分析多元正态或分类数据时出现的方程。方差分量的估计是通过类似于在正态性假设下用于限制最大似然（REML）估计的EM算法中所使用的表达式获得的。
