4 papers
Personalized Federated Learning for Tensor Regression
Kejun Chen, Xianqi Wei, Qianqian Zhu
The growing availability of tensor-valued data across multiple institutions creates opportunities for collaborative analysis, but also raises challenges related to data privacy, hi…
Private Federated Learning for High-dimensional Time Series
Kejun Chen, Qianqian Zhu
In the era of big data, leveraging information from multiple clients while preserving data privacy has emerged as a critical challenge in modern statistical modeling and forecastin…
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…
A robust and scalable estimation for high-dimensional volatility models
Kejun Chen, Yuchang Lin, Qianqian Zhu
This paper introduces a robust and computationally efficient estimation framework for high-dimensional volatility models in the BEKK-ARCH class. The proposed approach employs data…