标题： 贝叶斯变量选择用于单指标逻辑模型 显示英文标题

标题： Bayesian Variable Selection for Single Index Logistic Model

摘要： 在大数据时代，变量选择是处理小样本但协变量数量大的高维问题的关键技术。 不同的变量选择方法被提出用于不同的模型，例如线性模型、逻辑模型和广义线性模型。 然而，较少的研究关注单指标模型的变量选择，特别是单指标逻辑模型，这是由于未知的连接函数带来的困难以及传统逻辑模型中MCMC算法的慢混合率。 在本文中，我们通过利用高斯过程和数据增强的优势，提出了一个单指标逻辑模型的贝叶斯变量选择过程。 模拟和实际数据分析的数值结果表明，我们的方法优于现有技术。 显示英文摘要

摘要： 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.