The aim of this review is to provide a comprehensive survey of statistical challenges in
neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging …

For multisource data, blocks of variable information from certain sources are likely missing.
Existing methods for handling missing data do not take structures of block-wise missing data …

Block-wise missing data are becoming increasingly common in high-dimensional
biomedical, social, psychological, and environmental studies. As a result, we need efficient …

Alzheimer's (AD) is a progressive neurodegenerative disease frequently associated with
memory deficits and cognitive decline. Despite its irreversible once onset, some discoveries …

Medical imaging data have been widely used in modern health care, particularly in the
prognosis, screening, diagnosis, and treatment of various diseases. In this study, we …

We describe an approach to multivariate analysis, termed structured kernel principal
component regression (sKPCR), to identify associations in voxel-level connectomes using …

Multi-source functional block-wise missing data arise more commonly in medical care
recently with the rapid development of big data and medical technology, hence there is an …

We propose a covariate-adjusted subspace projection method for classifying functional data,
where the covariate effects on the response functions influence the classification outcome …

Given the diversity of real problems, complex data types such as partially ordered data, semi-
continuous data and neuroimaging data are frequently encountered in psychology …