Motivated by recent work on studying massive imaging data in various neuroimaging
studies, we propose a novel spatially varying coefficient model (SVCM) to capture the …

Motivated by recent work studying massive imaging data in the neuroimaging literature, we
propose multivariate varying coefficient models (MVCM) for modeling the relation between …

Many large-scale longitudinal imaging studies have been or are being widely conducted to
better understand the progress of neuropsychiatric and neurodegenerative disorders and …

Neuroimaging studies aim to analyse imaging data with complex spatial patterns in a large
number of locations (called voxels) on a two-dimensional surface or in a three-dimensional …

This article considers high-dimensional image-on-scalar regression, where the spatial
heterogeneity of covariate effects on imaging responses is investigated via a flexible …

An important problem in contemporary statistics is to understand the relationship among a
large number of variables based on a dataset, usually with p, the number of the variables …

Neuroimaging data collected at repeated occasions are gaining increasing attention in the
neuroimaging community due to their potential in answering questions regarding brain …

Neuroimaging faces the daunting challenge of multiple testing–an instance of multiplicity–
that is associated with two other issues to some extent: low inference efficiency and poor …

The use of imaging markers to predict clinical outcomes can have a great impact in public
health. The aim of this article is to develop a class of generalized scalar-on-image …

This paper proposes a generalized estimating equations approach for the analysis of
spatially correlated binary data when there are large numbers of spatially correlated …