Posterior Computations for Censored Regression Data
研究了正态和广义对数伽马误差下删失回归问题的后验密度计算与抽样，利用数据增强和重要性抽样方法，并展示了预测分布的计算。
Abstract This article describes the computation of and sampling from the posterior density for censored regression problems with normal and generalized log-gamma errors. The data augmentation algorithm (Tanner and Wong 1987) is facilitated in the normal error case because of the form of the augmented posterior. In the generalized log-gamma context, this simplicity is absent. The work of Sweeting (1981) is used as a motivation to develop an importance sampling scheme to sample from an augmented posterior. It is shown how the predictive distribution for a new observation may be computed and sampled from. The methodology is illustrated with two examples.