Modern scientific studies often collect datasets in the form of tensors. These datasets call for
innovative statistical analysis methods. In particular, there is a pressing need for tensor …

In recent years, longitudinal neuroimaging study has become increasingly popular in
neuroscience research to investigate disease-related changes in brain functions, to study …

We propose an evolutionary state space model (E-SSM) for analyzing high dimensional
brain signals whose statistical properties evolve over the course of a non-spatial memory …

Traditional clustering methods need to find the initial centers first. A reasonable cluster
center can improve the efficiency and accuracy of the algorithm. However, finding centers is …

We propose a Bayesian nonparametric matrix clustering approach to analyze the latent
heterogeneity structure in the shot selection data collected from professional basketball …

We propose a novel linear discriminant analysis (LDA) approach for the classification of high-
dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the …

Multi-view subspace clustering is the most widespread method of multi-view clustering.
However, most existing multi-view subspace clustering approaches posit that high …

Electroencephalography (EEG) studies produce region‐referenced functional data in the
form of EEG signals recorded across electrodes on the scalp. It is of clinical interest to relate …

Optimal estimation and computational limit of low-rank Gaussian mixtures Page 1 The
Annals of Statistics 2023, Vol. 51, No. 2, 646–667 https://doi.org/10.1214/23-AOS2264 © …

Tensor discriminant analysis is an important topic in tensor data analysis. However, given
the many proposals for tensor discriminant analysis methods, there lacks a systematic …