Speaker: Linglong Kong

Title: Doubly Adaptive Spatial Quantile Regression for Neuroimaging Data

Language: Chinese

Time & Venue: 2024.07.04 10:30-12:00 南楼205

Abstract: Numerous contemporary techniques for analyzing neuroimaging data commonly utilize least-squares estimation and Gaussian smoothing. Unfortunately, these approaches are not robust against imaging outliers and artifacts, which are generally unavoidable in practice, and fail to accommodate the sharp edges and various spatial scales of different neurological regions of interest. To address these issues and provide greater insight into the distribution of neuroimaging responses in a regression framework, we propose a doubly adaptive spatial quantile regression model (DASQRM). Our approach leverages information across both spatial locations and quantile levels in an adaptive fashion to robustly estimate model parameters and perform hypothesis testing. Furthermore, we rigorously establish important statistical properties of our proposed estimator and present an efficient method for model estimation. Through three simulation studies resembling real-world neuroimaging data together with an analysis of a dataset from the ADHD-200 Initiative, we demonstrate the benefits of our doubly adaptive approach in reducing estimator noise and bias, increasing statistical power and efficiency in hypothesis testing, and improving estimate interpretability.