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 …

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 …

Topological data analysis (TDA) approaches are becoming increasingly popular for studying
the dependence patterns in multivariate time series data. In particular, various dependence …

One fundamental challenge of data-driven analysis in neuroscience is modeling causal
interactions and exploring the connectivity between nodes in a brain network. Various …

We propose a two-stage approach Spec PC-CP to identify change points in multivariate time
series. In the first stage, we obtain a low-dimensional summary of the high-dimensional time …

This manuscript presents a novel method for discovering effective connectivity between
specified pairs of nodes in a high-dimensional network of time series. To accurately perform …

Topological data analysis (TDA) approaches are becoming increasingly popular for studying
the dependence patterns in multivariate time series data. In particular, various dependence …

Change point detection is a critical analysis in various scientific fields such as finance,
medicine, and climatology. Despite the recent developments in methods and algorithms, it …

Functional magnetic resonance imaging studies have long demonstrated a high degree of
correlated activity between the left and right hemispheres of the brain. Interregional …

Medir es comparar con un patrón, o clasificar en una escala, la magnitud de una
observación. Permite calibrar la importancia de un fenómeno y transmitirlo con exactitud …