Ridge Penalization in High-Dimensional Testing With Applications to Imaging Genetics

Iris Ivy Gauran, Gui Xue, Chuansheng Chen, Hernando Ombao, Zhaoxia Yu

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish (US)
JournalFrontiers in Neuroscience
Volume16
DOIs
StatePublished - Mar 24 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-04-14

ASJC Scopus subject areas

  • General Neuroscience

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