Statistics Seminar - Spring 2017

Seminar Schedule

Seminars are held on Thursdays at 4:00PM. Please note the location for each event in the schedule below, which will either be Cullimore 611 (CULM 611) or the Campus Center in Conference Room 230 (CTR 230). For questions about the seminar schedule, please contact Antai Wang.

Date Location Speaker, Affiliation, and Title Host
February 23 CULM 611 Rong Chen, Dept. of Statistics, Rutgers University
Factor Model for High Dimensional Matrix Valued Time Series

In finance, economics and many other field, observations in a matrix form are often observed over time. For example, many economic indicators are obtained in different countries over time. Various financial characteristics of many companies over time. Although it is natural to turn the matrix observations into a long vector then use standard vector time series models or factor analysis, it is often the case that the columns and rows of a matrix represent different sets of information that are closely interplayed. We propose a novel factor model that maintains and utilizes the matrix structure to achieve greater dimensional reduction as well as easier interpretable factor structure. Estimation procedure and its theoretical properties and model validation procedures are investigated and demonstrated with simulated and real examples.

Joint work with Dong Wang (Rutgers University) and Xialu Liu (San Diego State University)
Antai Wang
March 9 CTR 230 Pierre C. Bellec, Dept. of Statistics, Rutgers University
Optimistic Lower Bounds for Convex Regularized Least-Squares

Minimax lower bounds are pessimistic in nature: for any given estimator, minimax lower bounds yield the existence of a worst-case target vector β^∗_{worst} for which the prediction error of the given estimator is bounded from below. However, minimax lower bounds shed no light on the prediction error of the given estimator for target vectors different than β^∗_{worst}. A characterization of the prediction error of any convex regularized leastsquares is given. This characterization provide both a lower bound and an upper bound on the prediction error. This produces lower bounds that are applicable for any target vector and not only for a single, worst-case β^∗_{worst}.

Finally, these lower and upper bounds on the prediction error are applied to the Lasso is sparse linear regression. We obtain a lower bound involving the compatibility constant for any tuning parameter, matching upper and lower bounds for the universal choice of the tuning parameter, and a lower bound for the Lasso with small tuning parameter.
Antai Wang
March 22
CTR 235 Zhezhen Jin, Dept. of Biostatistics, Columbia University
Statistical Issues and Challenges in Biomedical Studies

In this talk, I will present statistical issues and challenges that I have encountered in my biomedical collaborative studies of item selection in disease screening, comparison and identification of biomarkers that are more informative to disease diagnosis, and estimation of weights on relatively importance of exposure variables on health outcome.

After a discussion on the issues and challenges with real examples, I will review available statistical methods and present our newly developed methods.
Antai Wang
March 30 CULM 611 Wei Biao Wu, Dept. of Statistics, University of Chicago
Asymptotic Theory for Quadratic Forms of High-Dimensional Data

I will present an asymptotic theory for quadratic forms of sample mean vectors of high-dimensional data. An invariance principle for the quadratic forms is derived under conditions that involve a delicate interplay between the dimension $p$, the sample size $n$ and the moment condition. Under proper normalization, central and non-central limit theorems are obtained. To perform the related statistical inference, I will propose a plug-in calibration method and a re-sampling procedure to approximate the distributions of the quadratic forms. The results will be applied multiple tests and inference of covariance matrix structures.
Yixin Fang & Antai Wang
April 6 CTR 230 Peter X. K. Song, Dept. of Biostatistics, University of Michigan Yixin Fang & Antai Wang
April 7
CTR 230 Yichuan Zhao, Dept. of Mathematics and Statistics, Georgia State U. Yixin Fang & Antai Wang
April 13 CULM 611 Haiyan Su, Dept. of Mathematical Sciences, Montclair State University Antai Wang
April 20 CTR 230 Hui Zhang, Dept. of Biostatistics, St. Jude Children’s Research Hospital Yixin Fang & Antai Wang
April 27 CULM 611 Hongyuan Cao, Dept. of Statistics, University of Missouri Yixin Fang & Antai Wang

Updated: March 27, 2017