Bayesian variable selection has experienced substantial developments over the past 30
years with the proliferation of large data sets. Identifying relevant variables to include in a …

Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that
provides an indirect measure of neuronal activity by detecting blood flow changes, has …

Neuroimaging data often take the form of high-dimensional arrays, also known as tensors.
Addressing scientific questions arising from such data demands new regression models that …

This book explores various state-of-the-art aspects behind the statistical analysis of
neuroimaging data. It examines the development of novel statistical approaches to model …

This work concerns spatial variable selection for scalar-on-image regression. We propose a
new class of Bayesian nonparametric models and develop an efficient posterior …

Online reviews provide rich information on customer satisfaction, displaying various numeric
ratings as well as detailed explanations presented in written form. However, analyzing such …

A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data Page 1 The Annals
of Applied Statistics 2016, Vol. 10, No. 2, 638–666 DOI: 10.1214/16-AOAS926 © Institute of …

A statistical pipeline for identifying physical features that differentiate classes of 3D shapes
Page 1 The Annals of Applied Statistics 2021, Vol. 15, No. 2, 638–661 https://doi.org/10.1214/20-AOAS1430 …

We propose a curve-based Riemannian geometric approach for general shape-based
statistical analyses of tumours obtained from radiologic images. A key component of the …

This work is motivated by multimodality breast cancer imaging data, which is quite
challenging in that the signals of discrete tumor-associated microvesicles are randomly …