Research Group - Statistics Core | Statistics & Data Science Graduate Interdisciplinary Program

Overview

The Core Statistics Group develops statistical methodology for complex and high-dimensional data.

Research Themes:

high-dimensional statistics
non-parametric methods
change-point analysis
Bayesian statistics

Members

Hao Helen Zhang

Nonparametric smoothing; Model selection; Data Mining; Statistical applications in biosciences and biomedicine.

Ning Hao

High dimensional data; Machine learning; Change point detection

Yue Selena Niu

Nonparametric statistics; Semiparametric modeling; Statistical genetics.

Xueying Tang

high dimensional Bayesian inference, latent variable models, small area estimation, psychometrics

Henry Scharf

spatiotemporal statistics; Bayesian statistics; Ecological applications

Select Publications

Zou H, Zhang HH. On the Adaptive Elastic-Net with a Diverging Number of Parameters. Ann Stat. 2009;37(4):1733-1751. doi: 10.1214/08-AOS625. PMID: 20445770; PMCID: PMC2864037.

Jianqing Fan, Shaojun Guo, Ning Hao, Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression, Journal of the Royal Statistical Society Series B: Statistical Methodology, Volume 74, Issue 1, January 2012, Pages 37–65, https://doi.org/10.1111/j.1467-9868.2011.01005.x

Niu, Y.S., Hao, N. and Zhang, H. (2016) Multiple Change-point Detection: a Selective Overview, Statistical Science, 31, 611-623. PDF