论文标题
具有隐藏混杂的广义线性模型的高维推断
High-Dimensional Inference for Generalized Linear Models with Hidden Confounding
论文作者
论文摘要
对高维回归模型的统计推断已被广泛研究其广泛的应用，从基因组学，神经科学到经济学。但是，实际上，通常存在与响应和协变量相关的潜在潜在的混杂因素，这可能导致标准证明方法的无效。本文着重于具有隐藏混杂性的广义线性回归框架，并提出了一种偏见的方法来解决这个高维问题，通过调整未衡量的混杂因素引起的效果。我们为提出的依据估计量建立了一致性和渐近正态性。该方法的有限样本性能通过广泛的数值研究和应用于遗传数据集的应用来证明。
Statistical inferences for high-dimensional regression models have been extensively studied for their wide applications ranging from genomics, neuroscience, to economics. However, in practice, there are often potential unmeasured confounders associated with both the response and covariates, which can lead to invalidity of standard debiasing methods. This paper focuses on a generalized linear regression framework with hidden confounding and proposes a debiasing approach to address this high-dimensional problem, by adjusting for the effects induced by the unmeasured confounders. We establish consistency and asymptotic normality for the proposed debiased estimator. The finite sample performance of the proposed method is demonstrated through extensive numerical studies and an application to a genetic data set.
