Generative Adversarial Networks are used for generating the data using a...
Topological data analysis (TDA) approaches are becoming increasingly pop...
Bayesian methods are commonly applied to solve image analysis problems s...
One of the goals of neuroscience is to study interactions between differ...
Brain connectivity reflects how different regions of the brain interact
...
In this paper we consider the filtering of partially observed
multi-dime...
In this paper we consider the filtering of partially observed
multi-dime...
Common measures of brain functional connectivity (FC) including covarian...
Current methods for clustering nodes over time in a brain network are
de...
In this paper we consider Bayesian parameter inference for partially obs...
Topological data analysis (TDA) approaches are becoming increasingly pop...
The Granger Causality (GC) test is a famous statistical hypothesis test ...
We propose a general class of INteger-valued Generalized AutoRegressive
...
To study the neurophysiological basis of attention deficit hyperactivity...
Over the last two decades, topological data analysis (TDA) has emerged a...
The premise of independence among subjects in the same cluster/group oft...
Brain functional connectivity (FC) reveals biomarkers for identification...
Intrinsic connectivity networks (ICNs) are specific dynamic functional b...
In this paper, a multivariate count distribution with Conway-Maxwell
(CO...
State-space models (SSM) with Markov switching offer a powerful framewor...
Local field potentials (LFPs) are signals that measure electrical activi...
Electroencephalograms (EEG) are noninvasive measurement signals of elect...
Topological data analysis, including persistent homology, has undergone
...
This paper presents a general framework for modeling dependence in
multi...
We propose a ridge-penalized adaptive Mantel test (AdaMant) for evaluati...
Optical imaging of genetically encoded calcium indicators is a powerful ...
Granger causality has been employed to investigate causality relations
b...
Multivariate time series data appear often as realizations of non-statio...
The standard approach to analyzing brain electrical activity is to exami...
We consider the challenges in extracting stimulus-related neural dynamic...
We propose a two-stage approach Spec PC-CP to identify change points in
...
Epilepsy is a chronic neurological disorder affecting more than 50 milli...
We propose a novel flexible bivariate conditional Poisson (BCP)
INteger-...
In this paper, we analyze electroencephalograms (EEG) which are recordin...
Objectives: To estimate sex ratio at birth (SRB) for the seven provinces...
Many experiments record sequential trajectories that oscillate around ze...
Within the neurosciences, to observe variability across time in the dyna...
We introduce a class of semiparametric time series models by assuming a
...
INteger Auto-Regressive (INAR) processes are usually defined by specifyi...
We present a unified statistical framework for characterizing community
...
We present a unified statistical framework for characterizing community
...
The sex ratio at birth (SRB) in India has been reported imbalanced since...
This article presents a new classification method for functional data. W...
A common class of methods for analyzing of multivariate time series,
sta...
This article presents a novel method for prediction of stationary functi...
Dynamic functional connectivity, as measured by the time-varying covaria...
We exploit altered patterns in brain functional connectivity as features...
This paper proposes a framework based on deep convolutional neural netwo...
Multichannel electroencephalograms (EEGs) have been widely used to study...
The goal of this paper is to develop a measure for characterizing comple...