论文标题
空间学生回归模型的客观贝叶斯分析
Objective Bayesian analysis for spatial Student-t regression models
论文作者
论文摘要
先前分布的选择是贝叶斯分析的关键方面。对于空间回归，从这个角度来看，使用客观贝叶斯分析框架，对参数的主观先前选择可能并不是微不足道的。空间学生-T回归模型在引起先验时提出了两个主要挑战：一个用于空间依赖参数，另一个用于自由度。众所周知，在客观先验上的后验分布的适当性并不能总是保证，而适当的先前分布的使用可能主导并偏向后验分析。在本文中，我们展示了我们提出的提议提议的提议提交给适当后验分布的条件。使用模拟研究以评估参考的性能，然后再使用含糊不清的事先。
The choice of the prior distribution is a key aspect of Bayesian analysis. For the spatial regression setting a subjective prior choice for the parameters may not be trivial, from this perspective, using the objective Bayesian analysis framework a reference is introduced for the spatial Student-t regression model with unknown degrees of freedom. The spatial Student-t regression model poses two main challenges when eliciting priors: one for the spatial dependence parameter and the other one for the degrees of freedom. It is well-known that the propriety of the posterior distribution over objective priors is not always guaranteed, whereas the use of proper prior distributions may dominate and bias the posterior analysis. In this paper, we show the conditions under which our proposed reference prior yield to a proper posterior distribution. Simulation studies are used in order to evaluate the performance of the reference prior to a commonly used vague proper prior.
