QIYANG HAN

Department of Statistics
Rutgers, the State University of New Jersey
110 Frelinghuysen Road
Piscataway, NJ 08854, United States

Office: Hill 408
Email:

About me

I am an assistant professor of Statistics at Rutgers University. I received a Ph.D. in Statistics from University of Washington under the supervision of Professor Jon A. Wellner. I am broadly interested in mathematical statistics and high dimensional probability, with a particular focus on empirical process theory and its applications to nonparametric and high dimensional statistics. My research is partially supported by an NSF grant and a CAREER award.

Papers

  (* = non alpha-order, multiple * = equal contribution)

• Q. Han and H. Ren. Gaussian random projections of convex cones: approximate kinematic formulae and applications.

• Q. Han. Exact spectral norm error of sample covariance.

• Q. Han and Y. Shen. Universality of regularized regression estimators in high dimensions.

• Q. Han. Noisy linear inverse problems under convex constraints: Exact risk asymptotics in high dimensions.

• Q. Han and Y. Shen. Generalized kernel distance covariance in high dimensions: Non-null CLTs and power universality.

• *O. Y. Feng, Y. Chen, Q. Han, R. J. Carroll and R. J. Samworth. Nonparametric, tuning-free estimation of S-shaped functions. J. Roy. Statist. Soc., Ser. B, to appear.

• Q. Han, T. Jiang and Y. Shen. Contiguity under high dimensional Gaussianity with applications to covariance testing. Ann. Appl. Probab., to appear.

• Q. Han, B. Sen and Y. Shen. High dimensional asymptotics of likelihood ratio tests in the Gaussian sequence model under convex constraints. Ann. Statist., 50, 376-406.

• H. Deng, Q. Han and B. Sen. Inference for local parameters in convexity constrained models. J. Amer. Statist. Assoc., to appear.

• *Y. Shen, Q. Han and F. Han. On a phase transition in general order spline regression. IEEE Trans. Inform. Theory, 68, 4043-4069.

• H. Deng, Q. Han and C.-H. Zhang. Confidence intervals for multiple isotonic regression and other monotone models. Ann. Statist., 49, 2021-2052.

• Q. Han and K. Kato. Berry-Esseen bounds for Chernoff-type non-standard asymptotics in isotonic regression. Ann. Appl. Probab., 32, 1459-1498.

• Q. Han. Set structured global empirical risk minimizers are rate optimal in general dimensions. Ann. Statist., 49, 2642-2671.

• Q. Han. Multiplier U-processes: sharp bounds and applications. Bernoulli, 28, 87-124.

• Q. Han and J. A. Wellner. Complex sampling designs: uniform limit theorems and applications. Ann. Statist., 49, 459-485.

• Q. Han and C.-H. Zhang. Limit distribution theory for block estimators in multiple isotonic regression. Ann. Statist., 48, 3251-3282.

• Q. Han and J. A. Wellner. Robustness of shape-restricted regression estimators: an envelope perspective.

• *Q. Han, *T. Wang, S. Chatterjee and R. J. Samworth. Isotonic regression in general dimensions. Ann. Statist., 47, 2440-2471.

• Q. Han and J. A. Wellner. Convergence rates of least squares regression estimators with heavy-tailed errors. Ann. Statist., 47, 2286-2319. (Presented in the Annals of Statistics special invited session at JSM 2019)

• Q. Han. Oracle posterior contraction rates under hierarchical priors. Electron. J. Statist., 15, 1085-1153.

• Q. Han and J. A. Wellner. Multivariate convex regression: global risk bounds and adaptation.

• Q. Han and J. A. Wellner. Approximation and estimation of s-concave densities via Rényi divergences. Ann. Statist., 44, 1332-1359.

• *A. Jalali, Q. Han, I. Dumitriu and M. Fazel. Exploiting tradeoffs for exact recovery in heterogeneous stochastic block models. NeurIPS 2016, 29.