# Research

#### Publications

- Q. Han, T. Jiang, and Y. Shen (2021) A general method for power analysis in testing high dimensional covariance matrices. To appear at
*Ann. Appl. Probab.*[arxiv] - C. Gao, Y. Shen, and A.Y. Zhang (2021) Uncertainty quantification in the Bradley-Terry-Luce model. To appear at
*Inf. Inference*[journal] [arxiv] - Y. Shen, Q. Han, and F. Han (2022) On a phase transition in general order spline regression.
*IEEE Trans. Inf. Theory*[journal][arxiv] - Q. Han, B. Sen, and Y. Shen (2022) High dimensional asymptotics of likelihood ratio tests in Gaussian sequence model under convex constraint.
*Ann. Statist.*[journal] [arxiv] - Y. Shen, C. Gao, D. Witten, and F. Han (2020) Optimal estimation of variance in nonparametric regression with random design.
*Ann. Statist.*[journal][arxiv] - Y. Shen, F. Han, and D. Witten (2020) Exponential inequalities for dependent V-statistics via random Fourier features.
*Electron. J. Probab.*[journal][arxiv]

#### Preprints

- X. Xu, Y. Shen, Y. Chi, and C. Ma (2023) The power of preconditioning in overparameterized low-rank matrix sensing. [arxiv]
- Y. Shen and Y. Wu (2022) Empirical Bayes estimation: When does g-modeling beat f-modeling in theory (and in practice)? [arxiv]
- Q. Han and Y. Shen (2022) Universality of regularized regression estimators in high dimensions. [arxiv]
- F. Han, Z. Miao, and Y. Shen (2021) Nonparametric mixture MLEs under Gaussian-smoothed optimal transport distance. [arxiv]
- Q. Han and Y. Shen (2021) Generalized kernel distance covariance in high dimensions: non-null CLTs and power universality. [arxiv]
- Y. Shen, F. Han, and D. Witten (2019) Tail behavior of dependent V-statistics and its applications. [arxiv]