多模态数据分析的归一化核去偏机器学习
Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis
摘要：
多模态成像已经改变了神经科学研究。虽然它带来了前所未有的机遇，但也带来了严峻的挑战。特别是，它是很难结合的优点，解释性归因于一个简单的关联模型的灵活性实现了高度自适应的非线性模型。在这篇文章中，我们提出了一个正交化核去偏机器学习方法，这是建立在奈曼正交性和分解正交性的形式，多模态数据分析。我们的目标是几乎所有多模态研究中自然出现的设置，其中有一个主要的感兴趣的模态，加上额外的辅助模态。我们建立了估计的主要参数的根$N$ -一致性和渐近正态性，半参数估计效率，以及预测的主要模态效应的置信带的渐近有效性。我们的建议享有，在很大程度上，模型的可解释性和模型的灵活性。它也与现有的多模态数据集成的统计方法以及基于正交性的高维推理方法有很大的不同。我们证明了我们的方法的有效性，通过模拟和应用程序的多模态神经影像学研究阿尔茨海默氏病。
英文摘要：
Multimodal imaging has transformed neuroscience research. While it presents unprecedented opportunities, it also imposes serious challenges. Particularly, it is difficult to combine the merits of the interpretability attributed to a simple association model with the flexibility achieved by a highly adaptive nonlinear model. In this article, we propose an orthogonalized kernel debiased machine learning approach, which is built upon the Neyman orthogonality and a form of decomposition orthogonality, for multimodal data analysis. We target the setting that naturally arises in almost all multimodal studies, where there is a primary modality of interest, plus additional auxiliary modalities. We establish the root-$N$-consistency and asymptotic normality of the estimated primary parameter, the semi-parametric estimation efficiency, and the asymptotic validity of the confidence band of the predicted primary modality effect. Our proposal enjoys, to a good extent, both model interpretability and model flexibility. It is also considerably different from the existing statistical methods for multimodal data integration, as well as the orthogonality-based methods for high-dimensional inferences. We demonstrate the efficacy of our method through both simulations and an application to a multimodal neuroimaging study of Alzheimer's disease.
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