1 citations · 1 across the 4 of their papers we have counts for
6 papers
Minimax and Adaptive Transfer Learning for Sparse Canonical Correlation Analysis
Bo Fu, Dandan Jiang
We develop a transfer learning framework for high-dimensional sparse canonical correlation analysis (CCA) with multiple heterogeneous source datasets. Our goal is to improve estima…
Unifiedly Efficient Inference on All-Dimensional Targets for Large-Scale GLMs
Bo Fu, Dandan Jiang
The scalability of Generalized Linear Models (GLMs) for large-scale, high-dimensional data often forces a trade-off between computational feasibility and statistical accuracy, part…
Transfer learning for high-dimensional Factor-augmented sparse linear model
Bo Fu, Dandan Jiang
In this paper, we study transfer learning for high-dimensional factor-augmented sparse linear models, motivated by applications in economics and finance where strongly correlated p…
Accelerating Randomized Algorithms for Low-Rank Matrix Approximation
Dandan Jiang, Bo Fu, Weiwei Xu
Randomized algorithms are overwhelming methods for low-rank approximation that can alleviate the computational expenditure with great reliability compared to deterministic algorith…
Nonlinear Principal Component Analysis with Random Bernoulli Features for Process Monitoring
Ke Chen, Dandan Jiang
The process generates substantial amounts of data with highly complex structures, leading to the development of numerous nonlinear statistical methods. However, most of these metho…
Universal Bootstrap for Spectral Statistics: Beyond Gaussian Approximation
Guoyu Zhang, Dandan Jiang, Fang Yao
Spectral analysis plays a crucial role in high-dimensional statistics, where determining the asymptotic distribution of various spectral statistics remains a challenging task. Due…