Scholar

Sidney Resnick

Google Scholar ID: YRaCSVkAAAAJ

Cornell University School of ORIE

Applied Probability and Statisticsnetworks modelingheavy tails

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6,023

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Background

Professional interests center in applied probability and sometimes cross into statistics.
Has worked on modeling questions for queues, storage facilities, extremes, data networks, and estimation problems for tails and non-standard time series models.
A recurrent theme is the influence of tails, especially heavy tails where large values shock the system.
Heavy-tailed modeling is increasingly important in data network modeling (explaining long-range dependence in traffic) and finance (for Value at Risk estimation).
The analytic basis for heavy-tailed modeling is the theory of regularly varying functions; the probabilistic foundation relies on stochastic point processes.
Tail estimation typically requires extrapolation beyond observed data and demands strong knowledge of probability, stochastic processes, and statistics.