Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Safikhani, Abolfazl"'
Autor:
Feng, Tianshu, Gnanaolivu, Rohan, Safikhani, Abolfazl, Liu, Yuanhang, Jiang, Jun, Chia, Nicholas, Partin, Alexander, Vasanthakumari, Priyanka, Zhu, Yitan, Wang, Chen
Human cancers present a significant public health challenge and require the discovery of novel drugs through translational research. Transcriptomics profiling data that describes molecular activities in tumors and cancer cell lines are widely utilize
Externí odkaz:
http://arxiv.org/abs/2407.04486
The paper introduces a flexible model for the analysis of multivariate nonlinear time series data. The proposed Functional Coefficients Network Autoregressive (FCNAR) model considers the response of each node in the network to depend in a nonlinear f
Externí odkaz:
http://arxiv.org/abs/2402.07373
We introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques. This framework addresses multiple objectives, including minimum variance, mean-variance, and the maximum Sh
Externí odkaz:
http://arxiv.org/abs/2303.12751
Autor:
Ma, Mingliang, Safikhani, Abolfazl
Deep neural networks are powerful tools to model observations over time with non-linear patterns. Despite the widespread use of neural networks in such settings, most theoretical developments of deep neural networks are under the assumption of indepe
Externí odkaz:
http://arxiv.org/abs/2210.11530
Regime shifts in high-dimensional time series arise naturally in many applications, from neuroimaging to finance. This problem has received considerable attention in low-dimensional settings, with both Bayesian and frequentist methods used extensivel
Externí odkaz:
http://arxiv.org/abs/2210.07456
Autor:
Bai, Yue, Safikhani, Abolfazl
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 designed for specified models (e.g. mean shift model, vector auto-reg
Externí odkaz:
http://arxiv.org/abs/2207.09007
The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City. Understanding the temporal pattern of subway ridership through statistical models is crucial during such sh
Externí odkaz:
http://arxiv.org/abs/2201.11951
The fast transmission rate of COVID-19 worldwide has made this virus the most important challenge of year 2020. Many mitigation policies have been imposed by the governments at different regional levels (country, state, county, and city) to stop the
Externí odkaz:
http://arxiv.org/abs/2112.10983
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 linear combination of neighboring nodes and a set of node-specific covar
Externí odkaz:
http://arxiv.org/abs/2110.09596
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 structure. We first address the problem of detecting a single change point usi
Externí odkaz:
http://arxiv.org/abs/2109.14783