9 citations · 36 across the 22 of their papers we have counts for
15 papers · 1 filter
Longitudinal Omics Data Analysis: A Review on Models, Algorithms, and Tools
Ali R. Taheriyoun, Allen Ross, Abolfazl Safikhani +2
Longitudinal omics data (LOD) analysis is essential for understanding the dynamics of biological processes and disease progression over time. This review explores various statistic…
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…
Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models
Yuhan Tian, Abolfazl Safikhani
Sequential (online) change-point detection involves continuously monitoring time-series data and triggering an alarm when shifts in the data distribution are detected. We propose a…
A Functional Coefficients Network Autoregressive Model
Hang Yin, Abolfazl Safikhani, George Michailidis
The paper introduces a flexible model for the analysis of multivariate nonlinear time series data. The proposed Functional Coefficients Network Autoregressive (FCNAR) model conside…
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm
Xiudi Li, Abolfazl Safikhani, Ali Shojaie
Regime shifts in high-dimensional time series arise naturally in many applications, from neuroimaging to finance. This problem has received considerable attention in low-dimensiona…
A unified framework for change point detection in high-dimensional linear models
Yue Bai, Abolfazl Safikhani
In recent years, change point detection for high dimensional data has become increasingly important in many scientific fields. Most literature develop a variety of separate methods…
A General Modeling Framework for Network Autoregressive Processes
Hang Yin, Abolfazl Safikhani, George Michailidis
The paper develops a general flexible framework for Network Autoregressive Processes (NAR), wherein the response of each node linearly depends on its past values, a prespecified li…
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…
Regularized Estimation in High-Dimensional Vector Auto-Regressive Models using Spatio-Temporal Information
Zhenzhong Wang, Abolfazl Safikhani, Zhengyuan Zhu +1
A Vector Auto-Regressive (VAR) model is commonly used to model multivariate time series, and there are many penalized methods to handle high dimensionality. However in terms of spa…
Inference on the change point in high dimensional time series models via plug in least squares
Abhishek Kaul, Stergios B. Fotopoulos, Venkata K. Jandhyala +1
We study a plug in least squares estimator for the change point parameter where change is in the mean of a high dimensional random vector under subgaussian or subexponential distri…
Uncertainty Estimation in Functional Linear Models
Tapabrata Maiti, Abolfazl Safikhani, Ping-Shou Zhong
Functional data analysis is proved to be useful in many scientific applications. The physical process is observed as curves and often there are several curves observed due to multi…
Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models
Abolfazl Safikhani, Ali Shojaie
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to allow for piecewise stationarity, where the model is allowed to change at…
Structural Break Detection in High-Dimensional Non-Stationary VAR models
Abolfazl Safikhani, Ali Shojaie
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to allow for piecewise stationarity, where the model is allowed to change at…