33 citations · 65 across the 8 of their papers we have counts for
9 papers
Reduced-Rank Autoregressive Models for Matrix Time Series
Han Xiao, Yuefeng Han, Rong Chen +1
Matrix time series is a series of matrix data observed over time. Analytical tools for such time series is needed in many applications in finance, economics, engineering and many o…
Threshold Tensor Factor Model in CP Form
Stevenson Bolivar, Rong Chen, Yuefeng Han
This paper proposes a new Threshold Tensor Factor Model in Canonical Polyadic (CP) form for tensor time series. By integrating a thresholding autoregressive structure for the laten…
Dynamic Matrix Factor Models for High Dimensional Time Series
Ruofan Yu, Rong Chen, Han Xiao +1
Matrix time series, which consist of matrix-valued data observed over time, are prevalent in various fields such as economics, finance, and engineering. Such matrix time series dat…
CP Factor Model for Dynamic Tensors
Yuefeng Han, Dan Yang, Cun-Hui Zhang +1
Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional str…
Tensor Principal Component Analysis in High Dimensional CP Models
Yuefeng Han, Cun-Hui Zhang
The CP decomposition for high dimensional non-orthogonal spiked tensors is an important problem with broad applications across many disciplines. However, previous works with theore…
Simultaneous Decorrelation of Matrix Time Series
Yuefeng Han, Rong Chen, Cun-Hui Zhang +1
We propose a contemporaneous bilinear transformation for a matrix time series to alleviate the difficulties in modeling and forecasting matrix time series when and/…
Rank Determination in Tensor Factor Model
Yuefeng Han, Rong Chen, Cun-Hui Zhang
Factor model is an appealing and effective analytic tool for high-dimensional time series, with a wide range of applications in economics, finance and statistics. This paper develo…
Tensor Factor Model Estimation by Iterative Projection
Yuefeng Han, Rong Chen, Dan Yang +1
Tensor time series, which is a time series consisting of tensorial observations, has become ubiquitous. It typically exhibits high dimensionality. One approach for dimension reduct…
High-dimensional Linear Regression for Dependent Data with Applications to Nowcasting
Yuefeng Han, Ruey S. Tsay
Recent research has focused on penalized least squares (Lasso) estimators for high-dimensional linear regressions in which the number of covariates is considerably lar…