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

Iris Ivy Gauran¹, Gui Xue², Chuansheng Chen³

¹Biostatistics Group, Computer, Electrical, Mathematical Sciences, and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Frontiers in Neuroscience

|April 11, 2022

Summary

This study introduces methods for selecting the optimal ridge penalty in high-dimensional hypothesis testing, enhancing statistical power in fields like imaging genetics. Findings improve identifying consequential genetic variations for brain connectivity.

Keywords:

genome-wide association studies high-dimensional testing imaging genetics neuroimaging ridge penalization

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Area of Science:

Genetics
Neuroscience
Statistics

Background:

High-dimensionality is common in scientific fields like imaging genetics, presenting challenges for statistical inference.
Identifying consequential genetic variations for neurological features requires robust statistical methods.
Penalized inference, particularly ridge penalty, is a key strategy for high-dimensional data analysis.

Purpose of the Study:

To propose and examine methods for selecting the optimal ridge penalty in high-dimensional hypothesis testing.
To improve the statistical power of ridge-penalized tests.
To determine factors influencing the optimal ridge penalty choice.

Main Methods:

Developing and evaluating a class of methods for optimal ridge penalty selection.
Investigating strategies to enhance the statistical power of ridge-penalized hypothesis tests.
Analyzing the determinants of the optimal ridge penalty.

Main Results:

Novel strategies for improving statistical power in ridge-penalized tests were identified.
Key factors determining the optimal ridge penalty for hypothesis testing were elucidated.
The proposed methods were applied to an imaging genetics study.

Conclusions:

The study provides effective methods for optimal ridge penalty selection in high-dimensional hypothesis testing.
Findings contribute to more accurate identification of genetic variations in complex biological systems.
The research has direct applications in imaging genetics and broader biological research.