A general framework for modeling dependence in multivariate time series is presented. Its
fundamental approach relies on decomposing each signal inside a system into various …

We propose a novel regularized mixture model for clustering matrix-valued data. The
proposed method assumes a separable covariance structure for each cluster and imposes a …

Disrupted functional connectivity patterns have been increasingly used as features in pattern
recognition algorithms to discriminate neuropsychiatric patients from healthy subjects. Deep …

The standard approach to analyzing brain electrical activity is to examine the spectral
density function (SDF) and identify frequency bands, defined a priori, that have the most …

We provide further details for each stage of the Conex–Connect method to model the
conditional extremal dependence on brain connectivity. In this supplement, we discuss the …

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 …

The historical and geographical spread from older to more modern languages has long
been studied by examining textual changes and in terms of changes in phonetic …

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 …

We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure
for characterizing connectivity in a network by clustering nodes or groups of channels that …

With the rapid growth of neuroimaging technologies, a great effort has been dedicated
recently to investigate the dynamic changes in brain activity. Examples include time course …