2 citations · 4 across the 5 of their papers we have counts for
5 papers
High-Dimensional Multivariate VAR Estimation with Spatio-Temporal Structure
Peiliang Bai
High-dimensional vector autoregressive (VAR) models provide a flexible framework for characterizing dynamic dependence in multivariate spatio-temporal systems, but their unrestrict…
Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models
Sepideh Mosaferi, Abolfazl Safikhani, Peiliang Bai
This paper addresses the problem of detecting change points in the spectral density of time series, motivated by EEG analysis of seizure patients. Seizures disrupt coherence and fu…
Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models
Peiliang Bai, Abolfazl Safikhani, George Michailidis
We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structur…
A Fast Detection Method of Break Points in Effective Connectivity Networks
Peiliang Bai, Abolfazl Safikhani, George Michailidis
There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop f…
Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package
Peiliang Bai, Yue Bai, Abolfazl Safikhani +1
Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics of multivariate time series data. They have been widely used in macroeconomics, financial econometrics, neuro…