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10/20/2022
Theoretical analysis of deep neural networks for temporally dependent observations
Deep neural networks are powerful tools to model observations over time ...
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10/14/2022
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm
Regime shifts in high-dimensional time series arise naturally in many ap...
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07/19/2022
A unified framework for change point detection in high-dimensional linear models
In recent years, change point detection for high dimensional data has be...
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01/28/2022
Non-Stationary Time Series Model for Station Based Subway Ridership During Covid-19 Pandemic (Case Study: New York City)
The COVID-19 pandemic in 2020 has caused sudden shocks in transportation...
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12/21/2021
Hybrid Modeling of Regional COVID-19 Transmission Dynamics in the U.S
The fast transmission rate of COVID-19 worldwide has made this virus the...
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10/18/2021
A General Modeling Framework for Network Autoregressive Processes
The paper develops a general flexible framework for Network Autoregressi...
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09/30/2021
Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models
We study the problem of detecting and locating change points in high-dim...
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09/30/2021
A Fast Detection Method of Break Points in Effective Connectivity Networks
There is increasing interest in identifying changes in the underlying st...
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05/23/2021
Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package
Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics o...
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12/18/2020
Regularized Estimation in High-Dimensional Vector Auto-Regressive Models using Spatio-Temporal Information
A Vector Auto-Regressive (VAR) model is commonly used to model multivari...
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07/03/2020
Inference on the change point in high dimensional time series models via plug in least squares
We study a plug in least squares estimator for the change point paramete...
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07/03/2020
Inference on the change point in high dimensional time series models via plug in least square
We study a plug in least squares estimator for the change point paramete...
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01/29/2018
Uncertainty Estimation in Functional Linear Models
Functional data analysis is proved to be useful in many scientific appli...
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12/06/2017
Predicting Short-Term Uber Demand Using Spatio-Temporal Modeling: A New York City Case Study
The demand for e-hailing services is growing rapidly, especially in larg...
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12/06/2017
Short-Term Prediction of Signal Cycle in Actuated-Controlled Corridor Using Sparse Time Series Models
Traffic signals as part of intelligent transportation systems can play a...
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11/28/2017
Spatio-temporal Modeling of Yellow Taxi Demands in New York City Using Generalized STAR Models
A highly dynamic urban space in a metropolis such as New York City, the ...
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11/17/2017