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. |
Affiliation | Dr. Linglong Kong is a professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a Canada Research Chair in Statistical Learning and a Canada CIFAR AI Chair. He is a fellow of American Statistical Association (ASA) and a fellow of the Alberta Machine Intelligence Institute (AMII). His publication record includes more than 100 peer-reviewed articles in top journals such as AOS, JASA and JRSSB as well as top conferences such as NeurIPS, ICML, ICDM, AAAI, and IJCAI. Dr. Kong currently serves as associate editor of the Journal of the American Statistical Association, the Canadian Journal of Statistics, and Statistics and its Interface. Additionally, Dr. Kong is a member of the Executive Committee of the Western North American Region of the International Biometric Society, chair of the ASA Statistical Computing Session program, and chair of the webinar committee. He served as a guest editor of Canadian Journal of Statistics and Statistics and its Interface, associate editor of International Journal of Imaging Systems and Technology, guest associate editor of Frontiers of Neurosciences, chair of the ASA Statistical Imaging Session, and member of the Statistics Society of Canada's Board of Directors. He is interested in the analysis of high-dimensional and neuroimaging data, statistical machine learning, robust statistics and quantile regression, as well as artificial intelligence for smart health. |