189 citations · 888 across the 107 of their papers we have counts for
11 papers · 1 filter
Adaptive Mantel Test for AssociationTesting in Imaging Genetics Data
Dustin Pluta, Hernando Ombao, Chuansheng Chen +3
Mantel's test (MT) for association is conducted by testing the linear relationship of similarity of all pairs of subjects between two observational domains. Motivated by applicatio…
Coherence-based Time Series Clustering for Brain Connectivity Visualization
Carolina Euan, Ying Sun, Hernando Ombao
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 tha…
Fisher information matrix of binary time series
Xu Gao, Hernando Ombao, Daniel Gillen
A common approach to analyzing categorical correlated time series data is to fit a generalized linear model (GLM) with past data as covariate inputs. There remain challenges to con…
Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States
Xu Gao, Babak Shahbaba, Hernando Ombao
Motivated by the problem of predicting sleep states, we develop a mixed effects model for binary time series with a stochastic component represented by a Gaussian process. The fixe…
Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices
Shiwei Lan, Andrew Holbrook, Gabriel A. Elias +3
Modeling correlation (and covariance) matrices can be challenging due to the positive-definiteness constraint and potential high-dimensionality. Our approach is to decompose the co…
Statistical Challenges in Modeling Big Brain Signals
Zhaoxia Yu, Dustin Pluta, Tong Shen +3
Brain signal data are inherently big: massive in amount, complex in structure, and high in dimensions. These characteristics impose great challenges for statistical inference and l…
A Robust Interrupted Time Series Model for Analyzing Complex Healthcare Intervention Data
Maricela Cruz, Miriam Bender, Hernando Ombao
Current health policy calls for greater use of evidence based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and…
Intrinsic data depth for Hermitian positive definite matrices
Joris Chau, Hernando Ombao, Rainer von Sachs
Nondegenerate covariance, correlation and spectral density matrices are necessarily symmetric or Hermitian and positive definite. The main contribution of this paper is the develop…
Multi-Scale Factor Analysis of High-Dimensional Brain Signals
Chee-Ming Ting, Hernando Ombao, Sh-Hussain Salleh
In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-…
Modeling high dimensional multichannel brain signals
Lechuan Hu, Norbert Fortin, Hernando Ombao
Our goal is to model and measure functional and effective (directional) connectivity in multichannel brain physiological signals (e.g., electroencephalograms, local field potential…
Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Chee-Ming Ting, Hernando Ombao, S. Balqis Samdin +1
Recent studies on analyzing dynamic brain connectivity rely on sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes…