Gauran Iris Ivy, Xue Gui, Chen Chuansheng, Ombao Hernando, Yu Zhaoxia
Biostatistics Group, Computer, Electrical, Mathematical Sciences, and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Center for Brain and Learning Science, Beijing Normal University, Beijing, China.
Front Neurosci. 2022 Mar 24;16:836100. doi: 10.3389/fnins.2022.836100. eCollection 2022.
High-dimensionality is ubiquitous in various scientific fields such as imaging genetics, where a deluge of functional and structural data on brain-relevant genetic polymorphisms are investigated. It is crucial to identify which genetic variations are consequential in identifying neurological features of brain connectivity compared to merely random noise. Statistical inference in high-dimensional settings poses multiple challenges involving analytical and computational complexity. A widely implemented strategy in addressing inference goals is penalized inference. In particular, the role of the ridge penalty in high-dimensional prediction and estimation has been actively studied in the past several years. This study focuses on ridge-penalized tests in high-dimensional hypothesis testing problems by proposing and examining a class of methods for choosing the optimal ridge penalty. We present our findings on strategies to improve the statistical power of ridge-penalized tests and what determines the optimal ridge penalty for hypothesis testing. The application of our work to an imaging genetics study and biological research will be presented.
高维性在诸如影像遗传学等各种科学领域中普遍存在，在影像遗传学中，人们研究了大量与大脑相关的基因多态性的功能和结构数据。与仅仅是随机噪声相比，确定哪些基因变异在识别大脑连通性的神经学特征方面具有重要意义至关重要。高维环境下的统计推断带来了涉及分析和计算复杂性的多重挑战。解决推断目标的一种广泛实施的策略是惩罚性推断。特别是，在过去几年中，岭惩罚在高维预测和估计中的作用一直受到积极研究。本研究通过提出并检验一类选择最优岭惩罚的方法，聚焦于高维假设检验问题中的岭惩罚检验。我们展示了关于提高岭惩罚检验统计功效的策略以及决定假设检验最优岭惩罚因素的研究结果。将介绍我们的工作在一项影像遗传学研究和生物学研究中的应用。
