论文标题
正常线性模型在正常逆伽马阶的正常线性模型下完全条件规范的联合分布特性
Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors
论文作者
论文摘要
完全条件规范（FCS）是一种方便且灵活的多重插补方法。它指定了一系列简单的回归模型，而不是缺失变量的潜在复杂关节密度。但是，FC可能不会收敛到固定分布。当条件模型的先验不明智时，许多作者研究了FCS的收敛性。我们扩展到了提供信息的案例。本文评估了正常线性模型与正常内gamma先验的收敛性。理论和仿真结果证明了FC的收敛性，并在关节模型下表明了先验规范的等效性，当分析模型是具有正常逆伽玛先验的线性回归时。
Fully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables. However, FCS may not converge to a stationary distribution. Many authors have studied the convergence properties of FCS when priors of conditional models are non-informative. We extend to the case of informative priors. This paper evaluates the convergence properties of the normal linear model with normal-inverse gamma prior. The theoretical and simulation results prove the convergence of FCS and show the equivalence of prior specification under the joint model and a set of conditional models when the analysis model is a linear regression with normal inverse-gamma priors.
