Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.

Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.
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DOI:
10.5705/ss.202020.0427
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发表时间:
2022-10
期刊:
影响因子:
1.4
通讯作者:
Kang, Jian
Kang, Jian
中科院分区:
数学3区
文献类型:
--
作者:
He, Jie;Kang, Jian

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变量筛选是一种有效的降维工具。然而,大多数现有的方法忽略了有用的先验知识,在特定的应用。在这项工作中,从贝叶斯建模的角度来看,我们开发了一个统一的变量筛选程序的线性回归模型。我们讨论了不同的构造后验均值筛选(PMS)统计,根据具体应用,将不同类型的先验知识。在无信息的先验规范下,PMS等价于高维普通最小二乘投影(HOLP)。我们建立了PMS与不同类型的先验知识的筛选一致性。我们表明,PMS是强大的先验误设定,当先验知识提供正确的信息,总结真实的参数设置,PMS可以大大提高选择精度相比,HOLP和其他现有的方法。我们说明了我们的方法与广泛的模拟研究和神经成像数据的分析。
Variable screening is a powerful and efficient tool for dimension reduction under ultrahigh dimensional settings. However, most existing methods overlook useful prior knowledge in specific applications. In this work, from a Bayesian modeling perspective, we develop a unified variable screening procedure for the linear regression model. We discuss different constructions of posterior mean screening (PMS) statistics to incorporate different types of prior knowledge according to specific applications. With non-informative prior specifications, PMS is equivalent to high-dimensional ordinary least-square projections (HOLP). We establish the screening consistency property for PMS with different types of prior knowledge. We show that PMS is robust to prior misspecifications; and when the prior knowledge provides correct information on summarizing the true parameter settings, PMS can substantially improve the selection accuracy compared to HOLP and other existing methods. We illustrate our method with extensive simulation studies and an analysis of neuroimaging data.
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