论文标题
在缺少协变量的情况下，用于空间数据的贝叶斯分层空间回归模型
Bayesian Hierarchical Spatial Regression Models for Spatial Data in the Presence of Missing Covariates with Applications
论文作者
论文摘要
在许多应用中，调查数据是从不同地区的不同调查中心收集的。碰巧在某些情况下，在协变量缺失值时，完全观察到响应变量。在本文中，我们提出了一个通过一维条件空间回归模型的序列，为响应变量和缺失协变量的关节空间回归模型。我们进一步构建了缺少协变量数据机制的联合空间模型。检查了所提出的模型的性质，并使用马尔可夫链蒙特卡洛采样算法从后验分布中采样。此外，开发了贝叶斯模型比较标准，修改后的偏差信息标准（MDIC）和伪 - 边缘可能性可能性（MLPML）的修改对数，以评估空间回归模型的空间回归模型的拟合。进行了广泛的仿真研究以检查所提出方法的经验性能。我们进一步应用了提出的方法来分析2011年中国健康和营养调查（CHN）的真实数据集。
In many applications, survey data are collected from different survey centers in different regions. It happens that in some circumstances, response variables are completely observed while the covariates have missing values. In this paper, we propose a joint spatial regression model for the response variable and missing covariates via a sequence of one-dimensional conditional spatial regression models. We further construct a joint spatial model for missing covariate data mechanisms. The properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. In addition, the Bayesian model comparison criteria, the modified Deviance Information Criterion (mDIC) and the modified Logarithm of the Pseudo-Marginal Likelihood (mLPML), are developed to assess the fit of spatial regression models for spatial data. Extensive simulation studies are carried out to examine empirical performance of the proposed methods. We further apply the proposed methodology to analyze a real data set from a Chinese Health and Nutrition Survey (CHNS) conducted in 2011.
