1 citations · 1 across the 5 of their papers we have counts for
6 papers
Multilevel Main Effects Matrix Factor Model
Kaixin Liu, Clifford Lam, Zetai Cen
We propose a multilevel main effects matrix factor model (MMEFM) to extract meaningful row and column effects from groups of matrix data. As a generalization of a multilevel matrix…
Main Effect Factor Models in High-Dimensional Matrix Time Series: Identification and Sparsity
Zetai Cen, Kaixin Liu, Clifford Lam
We propose a general identification framework for main effect factor models for matrix-valued time series. The classical sum-to-zero restriction on the row and column main effects…
On Testing Kronecker Product Structure in Tensor Factor Models
Zetai Cen, Clifford Lam
We propose a test for testing the Kronecker product structure of a factor loading matrix implied by a tensor factor model with Tucker decomposition in the common component. Through…
Inference on Dynamic Spatial Autoregressive Models with Change Point Detection
Zetai Cen, Yudong Chen, Clifford Lam
We analyze a varying-coefficient dynamic spatial autoregressive model with spatial fixed effects. One salient feature of the model is the incorporation of multiple spatial weight m…
Matrix-valued Factor Model with Time-varying Main Effects
Clifford Lam, Zetai Cen
We introduce the matrix-valued time-varying Main Effects Factor Model (MEFM). MEFM is a generalization to the traditional matrix-valued factor model (FM). We give rigorous definiti…
Tensor Time Series Imputation through Tensor Factor Modelling
Zetai Cen, Clifford Lam
We propose tensor time series imputation when the missing pattern in the tensor data can be general, as long as any two data positions along a tensor fibre are both observed for en…