Areas of Interest
Statistics, probability
Research
Large sample theory in statistics, characterization and construction of asymptotically efficient estimators and tests for semiparametric and nonparametric models, statistical inference for Markov chains and stochastic processes, estimation and comparison of curves, the behavior of plug-in estimators, optimal inference for bivariate distributions with constraints on the marginal, modelling with incomplete data, empirical likelihood, and theory and application of finite and infinite order U-statistics.
Courses
Fall 2026
| Course |
Section |
Title |
Schedule |
Room |
| Math 579 |
01 |
Advanced Statistical Inference |
TR 8:15–10:15
|
WH-329
|
Ph.D. Students
-
Mengyu Chen,
Spring, 2022
— An Empirical Likelihood Approach with Bivariate Data
-
Xiaojie Du,
Spring, 2018
— Inference in Linear Regression with Symmetric Errors: An Empirical Likelihood Approach
-
Ruiqi Liu,
Spring, 2018
— Identification and estimation in panel models with over specified number of groups
-
Nan Bi,
Fall, 2016
— Empirical Likelihood for a Class of Semiparametric Regression Models
-
Yilin Zhu,
Spring, 2016
— Estimation of the Error Distribution in a Varying Coefficient Regression Model
-
Peng Zhang,
Fall, 2010
— Prediction in heteroskedastic autoregressive models
-
Jichang Du,
Fall, 2007
— Covariate-matched estimator of the error variance in nonparametric regression
-
Jeffrey Forrester,
Summer, 2001
— Efficient Estimation of the Regression Parameter in a Heteroscedastic Regression Model Where Heteroscedasticity is Modeled as a function of the mean response
-
William Hooper,
Summer, 2001
— Efficient Estimation of Transformation Parameters in Nonparametric Regression
-
Hanxiang Peng,
Summer, 2001
— Efficient Estimation of Linear Functionals of a Bivariate Probability with Equal Marginals
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