1 citations · 1 across the 9 of their papers we have counts for
11 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…
Multiple Change Point Detection in the Mean and Covariance of High-Dimensional Matrix Time Series
Zetai Cen, Haeran Cho
This paper studies simultaneous change point detection in the first- and second-order structures of high-dimensional matrix-valued time series. Specifically, we extend the framewor…
Learning Perturbations to Extrapolate Your LLM
Zetai Cen, Chenfei Gu, Jin Zhu +3
Recent advancements in large language models demonstrate that injecting perturbations can substantially enhance extrapolation performance. However, current approaches often rely on…
Perturbation is All You Need for Extrapolating Language Models
Zetai Cen, Jin Zhu, Xinwei Shen +1
This paper develops a statistical theory of extrapolation for large language models, by reinterpreting them through pre-post-additive noise models. In contrast to the standard auto…
Detection and Mode-Identification of Multiple Change Points in Tensor Factor Models
Yuqi Zhang, Zetai Cen, Haeran Cho
We study the problem of modeling high-dimensional tensor-valued time series under a Tucker decomposition-based factor model while permitting the presence of multiple structural cha…
Identification and Estimation of Multi-order Tensor Factor Models
Zetai Cen
We propose a novel framework in high-dimensional factor models to simultaneously analyze multiple tensor time series, each with potentially different tensor orders and dimensionali…
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…