论文标题
贝叶斯变量选择在多元非线性回归中使用图结构
Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures
论文作者
论文摘要
高斯图形模型（GGM）是使用精确矩阵对依赖性结构进行概率探索的良好工具。我们开发了一种贝叶斯方法，将其在该GGMS设置中纳入的协变量信息将其纳入非线性看似无关的回归框架中。我们提出了一个联合预测指标和图形选择模型，并开发了有效的崩溃的吉布斯采样器算法来搜索关节模型空间。此外，我们研究了其理论变量选择属性。我们在各种模拟数据上演示了我们的方法，并以TCPA项目的真实数据集结尾。
Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices. We develop a Bayesian method to incorporate covariate information in this GGMs setup in a nonlinear seemingly unrelated regression framework. We propose a joint predictor and graph selection model and develop an efficient collapsed Gibbs sampler algorithm to search the joint model space. Furthermore, we investigate its theoretical variable selection properties. We demonstrate our method on a variety of simulated data, concluding with a real data set from the TCPA project.
