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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…