WEI BIAO WU 
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.
COLLABORATORS
X. Chen (Yale),
J.M. Comeron (Dept of Biological
Sciences, U. Iowa), S. Cs\"org\H{o} (U. Szeged, Hungary; Member,
Hungarian Academy of Sciences), Dana Draghicescu (CUNY), H. Erdogan
(EECS, U. Michigan), J.A. Fessler (EECS, U. Michigan), Bei Gou
(Electrical Engineering, U. Texas Arlington), Serge Guillas (Georgia
Tech.), Tailen Hsing (Statistics, U. Mich.), M. Kreitman (Ecology
and Evolution, U. Chicago), Wen-Hsiung Li (Ecology and Evolution, U.
Chicago), W. Liu, H.H.S. Lu (Institute of Statistics, National Chiao Tung
U., Taiwan), H. Ma (EECS, U. Michigan), G. Mentz (Statistics, U.
Michigan), G. Michailidis (Statistics, U. Michigan), J. Mielniczuk
(Computer Science Institute, Polish Academy of Sciences), W. Min
(IBM), M. Peligrad (Mathematics, U. Cincinnati), G. Mitra (Brunel
U., UK), M. Pourahmadi (Statistics, TAMU), Hong Qin, C.V.
Ravishankar (Computer Science \& Engineering, U.
California--Riverside), K.G. Shin (EECS, U. Michigan), X. Shao
(Statistics, UIUC), B. Valk\'o (R\'enyi Mathematics Institute,
Hungarian Academy of Sciences), S. Utev (Mathematical Sciences, U.
Nottingham, England), K.M. Wasserman (EECS,
U. Mich.), M. Woodroofe (Statistics, U. Mich.), Y. Yi (NYU),
Keming Yu (Brunel U., UK) Xingbin Yu (Electric Reliability
Council of Texas), D. Zhang (Qualcomm Inc., San Diego), Zhibiao Zhao
(Statistics, Penn State Univ.), Hui Zheng (Electrical Engineering,
U. Texas Arlington), Zhou Zhou (UChicago)