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March 2017

Hotelling Lectures: Aad van der Vaart, Leiden University

March 29 @ 3:30 pm - 5:00 pm

Nonparametric Bayesian methods: frequentist analysis Aad van der Vaart Leiden University A more detailed view of Bayesian methods to estimate functions or high-dimensional parameter vectors, and discuss the validity (or not) of these methods from a non-Bayesian point of view. For instance, we consider using a Gaussian process as a prior for a regression function or (after exponentiation and normalisation) for a density function. We characterise the rate at which the corresponding posterior distribution can recover a true function as…

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April 2017

STOR Colloquium: Hongtu Zhu, MD Anderson

April 3 @ 3:30 pm - 4:30 pm

Hongtu Zhu University of North Carolina at Chapel Hill, and The University of Texas MD Anderson Cancer Center   Title: Statistical Challenges, Opportunities, and Strategies in Large-Scale Medical Studies Abstract: With the rapid growth and clinical) in an unprecedented scale. The integration of such ultra high-dimensional data raises many statistical challenges, rendering most existing statistical methods and old data platform no longer suitable and thus underscoring the great need for methodological developments from a rigorous perspective. To address these challenges,…

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STOR Colloquium: Philip Ernst, Rice University

April 10 @ 3:30 pm - 4:30 pm

Philip Ernst Rice University   Title: Yule's "Nonsense Correlation" Solved!   Abstract: In this talk, I will discuss how I recently resolved a longstanding open statistical problem. The problem, formulated by the British statistician Udny Yule in 1926, is to mathematically prove Yule's 1926 empirical finding of ``nonsense correlation.”  We solve the problem by analytically determining the second moment of the empirical correlation coefficient of two independent Wiener processes. Using tools from Fredholm integral equation theory, we calculate the second…

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STOR Colloquium: Ilse Ipsen, North Carolina State University

April 17 @ 3:30 pm - 4:30 pm

Ilse Ipsen North Carolina State University Randomized Algorithms for Matrix Computations   The emergence of massive data sets, over the past fifteen or so years, has lead to the development of Randomized Numerical Linear Algebra. Fast and accurate randomized matrix algorithms are being designed for applications like machine learning, population genomics, astronomy, nuclear engineering, and optimal experimental design.  

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