论文标题
单索引逻辑模型的贝叶斯变量选择
Bayesian Variable Selection for Single Index Logistic Model
论文作者
论文摘要
在大数据时代，可变选择是处理样本量较小但大量协变量的高维问题的关键技术。提出了针对不同模型的不同变量选择方法，例如线性模型，逻辑模型和广义线性模型。但是，由于未知链接函数的困难以及MCMC算法的缓慢混合速率引起了传统logistic模型的难度，因此更少的工作重点是单个索引模型的可变选择，尤其是对于单个索引逻辑模型。在本文中，我们通过利用高斯流程和数据增强，为单个索引逻辑模型提出了一个贝叶斯变量选择程序。模拟和实际数据分析的数值结果显示了我们方法比艺术状态的优势。
In the era of big data, variable selection is a key technology for handling high-dimensional problems with a small sample size but a large number of covariables. Different variable selection methods were proposed for different models, such as linear model, logistic model and generalized linear model. However, fewer works focused on variable selection for single index models, especially, for single index logistic model, due to the difficulty arose from the unknown link function and the slow mixing rate of MCMC algorithm for traditional logistic model. In this paper, we proposed a Bayesian variable selection procedure for single index logistic model by taking the advantage of Gaussian process and data augmentation. Numerical results from simulations and real data analysis show the advantage of our method over the state of arts.
