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STOR Colloquium: Holger Rootzén, Chalmers University of Technology

October 25 @ 3:30 pm - 4:30 pm

Human life is unlimited -- but short   Does the human lifespan have an impenetrable biological upper limit which ultimately will stop further increase in life lengths? Answers to this question are important for our understanding of the aging process, and for the organization of society, and have led to intense controversies. Demographic data for  humans  have been interpreted as showing existence of a limit  close to the age, 122.45 years, of the  longest living documented human, Jeanne Calment, or…

Probability Seminar: Natalie Stanley, UNC-CH

October 26 @ 4:15 pm - 5:15 pm

Compressing Networks with Super Nodes Community detection is a commonly used technique for identifying cohesive groups of nodes in a network, based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the original network as a smaller network of 'super nodes', where each super node is comprised of one of more nodes of the original network. We can then use this super node representation as the input into standard community detection algorithms. To define the…

STOR Colloquium; Xiao-Li Meng, Harvard

November 6 @ 3:30 pm - 4:30 pm

STOR Colloquium: Todd Kuffner, Washington University in St. Louis

November 15 @ 3:30 pm - 4:30 pm

Title: Philosophy of Science, Principled Statistical Inference, and Data Science   Abstract: Statistical reasoning and statistical inference have strong historical connections with philosophy of science. In this talk, the new paradigm of data-driven science is examined through comparison with principled statistical approaches. I will review the merits and shortcomings of principled statistical inference. The talk will feature a case study of post-selection inference, recent progress regarding inference for black box algorithms, and a survey of future challenges.   Refreshments will…

STOR Colloquium: Alex Belloni, Duke

November 20 @ 3:30 pm - 4:30 pm

Title: Inference with High-Dimensional Controls and Parameters of Interest based on joint work with Victor Chernozhukov, Denis Chetverikov, and Ying Wei Abstract: In this work we propose and analyze procedures to construct confidence regions for p (infinite dimensional) parameters of interest after model selection for general moment condition models where p is potentially larger than the sample size n. This allows us to cover settings with functional response data where each of the p > n parameters of interest is…

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