Bayesian Inference in Multivariate Regression With Missing Observations on the Response Variables
讨论多变量线性模型中响应变量存在嵌套模式缺失时的贝叶斯推断方法，通过纳入缺失观测的预测密度来估计回归系数，并用加拿大经济数据演示。
We discuss the case of the multivariate linear model Y = XB + E with Y an (n × p) matrix, and so on, when there are missing observations in the Y matrix in a so-called nested pattern. We propose an analysis that arises by incorporating the predictive density of the missing observations in determining the posterior distribution of B, and its mean and variance matrix. This involves us with matric-T variables. The resulting analysis is illustrated with some Canadian economic data.