Resnick joined the Cornell faculty in 1987 after nine years at Colorado State University, six years at Stanford University, and two years at the Technion, in Haifa, Israel. He has also held visiting appointments at several institutions, including the University of Amsterdam and the Amsterdam Mathematics Center; the Australian National University and CSIRO, in Canberra, Australia; the Technion in Israel (as a Lady Davis Fellow); Sussex University, in Brighton, UK (as a Science and Engineering Research Council Fellow), Erasmus University, in Rotterdam, The Netherlands; and ETH Zurich. Resnick is a fellow of the Institute of Mathematical Statistics, and while at Colorado State was an Oliver Pennock Distinguished Service Award winner. He is a founding associate editor of Annals of Applied Probability, and a former associate editor of Journal of Applied Probability, Stochastic Models, Extremes and The Mathematical Scientist, Stochastic Processes and Their Applications. He served a three-year term on the Council of the Institute of Mathematical Statistics and served on their ad hoc committee on electronic publishing. He is a former editor for Birkhauser, Boston serving on the boards of the Progress in Probability and Progress in Probability and Its Applications series and also serves on the editorial board of the Springer series Operations Research and Financial Engineering. Resnick concluded a five year term as Director of Cornell's School of Operations Research and Information Engineering in July 2003. He was named Lee Teng-Hui Professor in Engineering in November 2008 and became e meritus in 2019. He has authored or coauthored approximately 195 papers and five books.
Talk: Distinguishing Forms of Asymptotic Dependence in Heavy Tailed Data
Abstract: Graphical and exploratory techniques for distinguishing forms of asymptotic dependence in heavy tailed data are often inconclusive. This is especially a problem when choosing appropriate preferential attachment models for network data since the asymptotic dependence is very different depending on whether reciprocity is present or not. We have formulated several test statistics to formalize the process of identifying the form of the dependence but these depend on unknown parameters. If unknown parameters are replaced by plug-in estimators, the tests fail. However, bootstrap techniques are successful as examples show.