Homepage of Wei Biao Wu

WEI BIAO WU

Office: 5734 S. University Ave, Department of Statistics, The University of Chicago, Chicago, IL 60637
E-mail: wbwu@galton.uchicago.edu
My homepage at University of Chicago
My homepage at University of Michigan

PROFESSIONAL EXPERIENCE

Assistant Professor, Department of Statistics, The University of Chicago (July 1, 2001-present)
Instructor, Stochastic Processes, EECS 502 and Aerospace 553 (graduate class), Winter, 2001, EECS and Aerospace Engineering departments.
Research Assistant, Department of Statistics, University of Michigan (Summer 99-present). Mainly work with M. Woodroofe on time series analysis, and collaboration with other professors from various fields such as electrical engineering and computer science.
Teaching Assistant, Department of Statistics, University of Michigan (Summer 98, Fall 98, Winter 99). Taught Introduction to Statistics and Data Analysis. The main duty was leading two lab sessions with SPSS.

PROFESSIONAL SERVICE

Associate Editor, Bernoulli, journal of the Bernoulli Society, International Statistical Institute.

Associate Editor, Annals of Statistics, IMS

RESEARCH INTERESTS

In the study of random processes, dependence plays a fundamental role. By interpreting random processes as physical systems, I introduced physical dependence coefficients that quantify the degree of dependence of outputs on inputs. Such dependence measures are related to the nonlinear system theory and riskmetrics. They provide a new framework for the study of random processes and shed new light on a variety of problems including estimation of linear models with dependent errors, nonparametric inference of time series, representations of sample quantiles, bootstrap for time series, spectral estimation among others. This work is published at Wu (October, 2005): Nonlinear system theory: Another look at dependence, Proceedings of the National Academy of Sciences.

I am currently interested in estimating covariance matrices of temporally observed series. The latter problem is quite important in the study of functional and longitudinal data. On the other hand, however, this problem is notoriously difficult since (i) one needs to estimate as many as n(n+1)/2 unknowns for a covariance matrix and (ii) a covariance matrix is intrinsically positive definite if the underlying random vector is linearly independent. This work is joint with Mohsen Pourahmadi.

Here is a list of my papers, which cover my several recent areas of interest.

MY FAVORITE: CHINESE NEW YEAR SPECTACULAR. It fuses the beloved, age-old traditions and virtues of ancient China with the best artistic techniques of the West.

CO-AUTHORS AND ASSOCIATES

S. Csorgo (Member, Hungarian Academy of Sciences and Professor, University of Szeged, Hungary), H. Erdogan (EECS, UM), J.A. Fessler(Professor, EECS, UM), T. Hsing (Statistics, TAMU), R. Keener (Professor, Statistics, UM), H. Ma (EECS, UM), G. Mentz (Statistics, UM), G. Michailidis (Statistics, UM), J. Mielniczuk (Polish Academy of Sciences), M. Pourahmadi (Statistics, NIU), C.V. Ravishankar (Computer Science \& Engineering Dept., Univ. of California - Riverside), M. Woodroofe (Savage Professor, Statistics, UM), D. Zhang (EECS, UM, and Qualcomm, San Diego)