3 citations · 7 across the 16 of their papers we have counts for
4 papers · 1 filter
High-dimensional Autoregressive Modeling for Time Series with Hierarchical Structures
Lan Li, Shibo Yu, Yingzhou Wang +1
Modern applications have made ubiquitous high-dimensional data, especially time-dependent data, with more and more complicated structures, and it also has become more frequent to e…
Improving time series estimation and prediction via transfer learning
Yuchang Lin, Qianqian Zhu, Guodong Li
There are many time series in the literature with high dimension yet limited sample sizes, such as macroeconomic variables, and it is almost impossible to obtain efficient estimati…
High-dimensional low-rank matrix regression with unknown latent structures
Di Wang, Xiaoyu Zhang, Guodong Li +1
We study low-rank matrix regression in settings where matrix-valued predictors and scalar responses are observed across multiple individuals. Rather than assuming a fully homogeneo…
An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series
Yuxi Cai, Lan Li, Yize Wang +1
In autoregressive modeling for tensor-valued time series, Tucker decomposition, when applied to the coefficient tensor, provides a clear interpretation of supervised factor modelin…