# Colloquia

Unless otherwise noted, all talks are in 120 Hanes Hall, at 3:30 PM on Mondays and Wednesdays. Prior to the talk, from 3:00-3:30 PM, the audience is invited for refreshments in the lounge on the 3rd floor of Hanes Hall. If you would like to suggest a speaker, or get on our mailing list, please send an email to Dr. Gabor Pataki or Dr. Vladas Pipiras.

In addition to weekly colloquia and seminars, Hotelling lectures are held to honor the memory of Professor Harold Hotelling, first chairman of the “Department of Mathematical Statistics.”

Quick access to previous talks:

## Past Events › STOR Colloquium

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## STOR Colloquium: Santiago Balseiro, Duke University

Santiago Balseiro Duke University Dynamic Mechanisms with Martingale Utilities We study the dynamic mechanism design problem of a seller who repeatedly sells independent items to a buyer with private values. In this setting, the seller could potentially extract the entire buyer surplus by running efficient auctions and charging an upfront participation fee at the beginning of the horizon. In some markets, such as internet advertising, participation fees are not practical since buyers expect to inspect items before purchasing them. This…

Find out more »## STOR Colloquium: Julie Ivy, North Carolina State University

Julie Ivy North Carolina State University To be Healthy, Wealthy, and Wise: Using Decision Modeling to Personalize Policy in Health, Hunger Relief, and Education Decision making to satisfy the basic human needs of health, food, and education is complex, accomplishing this in such a way that solutions are personalized to the needs of the individual has been the focus of my research. This talk will present an overview of my research in the area of decision making under conditions…

Find out more »## STOR Colloquium: Peter Hoff, Duke University

Peter Hoff Duke University Adaptive FAB confidence intervals with constant coverage Abstract: Confidence intervals for the means of multiple normal populations are often based on a hierarchical normal model. While commonly used interval procedures based on such a model have the nominal coverage rate on average across a population of groups, their actual coverage rate for a given group will be above or below the nominal rate, depending on the value of the group mean. In this talk…

Find out more »## STOR Colloquium: Gudrun Johnsen, University of Iceland

Gudrun Johnsen University of Iceland Title: If you look close enough you can see a whole lot: Data collection and analysis of the Parliamentary Investigation Commission looking into the Icelandic Banking Collapse in 2008 Abstract: The combined collapse of Iceland's three largest banks is the third largest bankruptcy in history and the largest banking system collapse suffered by any country in modern economic history, relative to GDP. In 2008 the Icelandic Parliament installed a Special Investigation Commission (SIC)…

Find out more »## STOR Colloquium: Mariana Olvera-Cravioto, University of California-Berkeley

Directed complex networks and ranking algorithms In the first part of this talk I will discuss a family of inhomogeneous directed random graphs for modeling complex networks such as the web graph, Twitter, ResearchGate, and other social networks. This class of graphs includes as a special case the classical Erdos-Renyi model, and can be used to replicate almost any type of predetermined degree distributions, in particular, power-law degrees such as those observed in most real-world networks. I will mention during…

Find out more »## STOR Colloquium: Zhiyi Zhang, UNC Charlotte

Zhiyi Zhang University of North Carolina at Charlotte Title: Statistical Implications of Turing’s Formula Abstract: This talk is organized into three parts. Turing’s formula is introduced. Given an iid sample from a countable alphabet under a probability distribution, Turing’s formula (introduced by Good (1953), hence also known as the Good-Turing formula) is a mind-bending non-parametric estimator of total probability associated with letters of the alphabet that are NOT represented in the sample. Many of its statistical properties were…

Find out more »## STOR Colloquium: Hongtu Zhu, MD Anderson

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 With the rapid growth of modern technology, many biomedical studies have collected data across different sources (e.g., imaging, genetics, 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…

Find out more »## CANCELLED STOR Colloquium

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…

Find out more »## STOR Colloquium: Ilse Ipsen, North Carolina State University

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 led 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. We give a flavour of randomized algorithms for the solution of least squares/regression problems and, if time permits, for the computation of…

Find out more »## STOR Colloquium: Jong-Shi Pang, U. of Southern California

Jong-Shi Pang University of Southern California Title: Structural Properties of Affine Sparsity Constraints Abstract: We introduce a new constraint system for sparse variable selection in statistical learning. Such a system arises when there are logical conditions on the sparsity of certain unknown model parameters that need to be incorporated into their selection process. Formally, extending a cardinality constraint, an affine sparsity constraint (ASC) is defined by a linear inequality with two sets of variables: one set of continuous variables…

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