论文标题
通过Potts-Gibbs随机分区模型的贝叶斯非参数标量图回归
Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models
论文作者
论文摘要
标量形图像回归旨在根据高维成像数据来研究兴趣标量响应的变化。我们提出了一种新型的贝叶斯非参数标量标量回归模型，该模型利用体素的空间坐标来组对响应的体素，对响应的影响与具有共同系数的响应相似。我们使用POTTS-GIBBS随机分区模型作为在空间依赖分区过程的随机分区的先验中，从而鼓励代表空间连续区域的组。此外，还利用贝叶斯收缩率来识别与预测最相关的协变量和区域。使用模拟数据集说明了所提出的模型。
Scalar-on-image regression aims to investigate changes in a scalar response of interest based on high-dimensional imaging data. We propose a novel Bayesian nonparametric scalar-on-image regression model that utilises the spatial coordinates of the voxels to group voxels with similar effects on the response to have a common coefficient. We employ the Potts-Gibbs random partition model as the prior for the random partition in which the partition process is spatially dependent, thereby encouraging groups representing spatially contiguous regions. In addition, Bayesian shrinkage priors are utilised to identify the covariates and regions that are most relevant for the prediction. The proposed model is illustrated using the simulated data sets.
