Grants:

Preprints:

Network Regression and Supervised Centrality Estimation.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3963523

https://arxiv.org/abs/2111.12921



Optimal functional bilinear regression with matrix covariates via reproducing kernel Hilbert space.

http://arxiv.org/abs/2311.12597


Multi-Group Multi-Subject Independent Component Analysis with Application in Global Portfolio Management

Publications:

Tensor factor model estimation by iterative projection.

Annals of Statistics, accepted

https://arxiv.org/abs/2006.02611


CP Factor Model for Dynamic Tensors.

The Journal of the Royal Statistical Society, Series B, accepted


Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data.

Journal of Econometrics, 232(2), 544-564

https://arxiv.org/abs/2006.16501


Discussion of ‘Vintage Factor Analysis with Varimax Performs Statistical Inference’ by Rohe & Zeng.

Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85(4), 1076–1080


Rejoinder: Factor models for high-dimensional tensor time series.

Journal of the American Statistical Association, Vol.117 (537), p.128-132


Factor models for high-dimensional tensor time series (with discussion).

Journal of the American Statistical Association, Vol.117 (537), p.94-116 

https://arxiv.org/abs/1905.07530


Autoregressive models for matrix-valued time series.

Journal of Econometrics, 222(1):539-560.

https://arxiv.org/abs/1812.08916


The Power of Clinical Data Empowered by Clinical Prediction Model

Annals of Translational Medicine, 8, 1-4.


Rate optimal denoising of simultaneously sparse and low rank matrices.

Journal of Machine Learning Research, Vol.17(92), pp.1-27. 

https://arxiv.org/abs/1405.0338


Supervised Singular Value Decomposition and Its Asymptotic Properties. 

Journal of Multivariate Analysis, 146:7-17.


A sparse singular value decomposition method for high-dimensional data. 

Journal of Computational and Graphical Statistics, 23(4):923-942.


Optimal matching with minimal deviation from fine balance in a study of obesity and surgical outcomes. 

Biometrics, Volume 68, Issue 2, pages 628-636.


An R package and a study of methods for computing empirical likelihood. 

Journal of Statistical Computation and Simulation, 83(7), 1363-1372.

Work in progress: