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Professor David Madigan, Associate Dean, Faculty of
Arts and Sciences, Rutgers University
Professor Regina
Liu, Chair, Department of Statistics, Rutgers
University
Professor John Kolassa,
Graduate Director, Department of
Statistics, Rutgers University
Professor Harold
Sackrowitz, Undergraduate Director, Department
of Statistics, Rutgers University
Professor Javier Cabrera, Director, Institute of Biostatistics,
Department of Statistics, Rutgers University
Faculty
and Areas of Expertise
·
Robert H. Berk,
Ph.D. Harvard. Sequential analysis, nonparametric statistics, large
sample theory, non- and semi-parametric methods, robust techniques,
quality control.
·
Steven Buyske,
Ph.D. Brown (Mathematics); Ph.D. Rutgers
(Statistics). Statistical genetics, biostatistics, psychometrics,
experimental design.
·
Javier Cabrera, Ph.D. Princeton.
Statistical computing, graphical methods, computer vision, directional
data analysis.
·
Arthur Cohen, Ph.D. Columbia.
Decision theory, linear models.
·
Richard Gundy, Ph.D. Indiana Univ.
(experimental psychology). Ph.D. University of Chicago (statistics).
Probability theory, harmonic analysis.
·
Donald Hoover,
Ph.D. Stanford; MPH UCLA. Clinical trials, epidemiology, longitudinal
methods, group randomization and multiple comparisons.
·
Rebecka Jornsten, Ph.D. UC Berkeley. Applied statistics,
image and signal processing, time series analysis.
·
John Kolassa,
Ph.D. Univ. Chicago. Asymptotics, biostatistics.
·
Juan Lin, Ph.D., Univ. Chicago.
Multivariate analysis, probabilistic networks, machine learning.
·
Regina
Y. Liu, Ph.D. Columbia.
Nonparametric inferences, resampling, data
depth, text mining, statistical quality control.
·
David Madigan, Ph.D. Trinity College, Dublin.
Data mining, statistical computing, Bayesian data analysis, graphical
Markov models.
·
Joseph Irwin Naus,
Ph.D. Harvard. Applied probability, sampling theory, data quality
control, clustering and coincidence models,
matching in DNA sequences.
·
Harold B. Sackrowitz,
Ph.D. Columbia. Statistical
inference and decision theory, finite action problems, order restricted
inference.
·
Lawrence
Shepp, Ph.D. Princeton.
Mathematical models in economics and finance, pure and applied probability,
tomography and medical imaging.
·
Kesar Singh,
Ph.D. Indian Statistical Institute. Nonparametric statistics, asymptotics, large deviations. Resampling
procedures: bootstrap and jackknife, notions of
data depth, angular data. Confidence distributions. Mathematical finance.
·
William E. Strawderman,
Ph.D. Rutgers. Decision theory, Bayesian analysis,
multivariate statistics.
·
David E. Tyler, Ph.D. Princeton.
Multivariate analysis, robust techniques, directional data, computer
vision and time series.
·
Minge Xie, Ph.D.
Illinois. Longitudinal data
analysis, modeling and statistical inference, with applications to
biomedical sciences, social sciences and industries.
·
Cun-Hui Zhang,
Ph.D. Columbia. Empirical Bayes methods,
survival analysis, statistical inference and probability theory.
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