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August 2017

STOR Colloquium: Ion Necoara, Polytechnic University of Bucharest

August 28, 2017 @ 3:30 pm - 4:30 pm

Ion Necoara Polytechnic University of Bucharest   Conditions for linear convergence of (stochastic) first order methods   For convex optimization problems deterministic first order methods have linear convergence when the objective function is smooth and strongly convex. Moreover, under the same conditions - smoothness and strong convexity - sublinear convergence rates have been derived for stochastic first order methods. However, in many applications (machine learning, statistics, control, signal processing)   the strong convexity condition does not hold, but the objective function…

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September 2017

STOR Colloquium: Hao Chen, UC Davis

September 6, 2017 @ 3:30 pm - 4:30 pm

Change-point detection for locally dependent data   Local dependence is common in multivariate and non-Euclidean data sequences, such as network data. We consider the testing and estimation of change-points in such sequences. A new way of permutation, circular block permutation with a randomized starting point, is proposed and studied for a scan statistic utilizing graphs representing the similarity between observations. The proposed permutation approach could correctly address for local dependence and make it possible the theoretical treatments for the non-parametric…

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STOR Colloquium: Iain Carmichael, UNC-Chapel Hill

September 11, 2017 @ 3:30 pm - 4:30 pm

Title: Data science and the undergraduate curriculum   Last semester we developed and taught a new course titled “Introduction to Data Science” for the undergraduate analytics major at UNC (see https://idc9.github.io/stor390/). The core topics of the class were: programming in R (working with data, visualization, functions/loops/conditionals), data analysis (exploratory, predictive and inferential), acquiring data (APIs, web scraping), communication (e.g. literate programming, effective visualization), and some additional topics (text data and data ethics/inequality).  This course differed from existing courses in a…

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STOR Colloquium: Philip Ernst, Rice

September 18, 2017 @ 3:30 pm - 4:30 pm

Yule's "Nonsense Correlation" Solved! 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 moment of the empirical correlation to obtain a…

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October 2017

STOR Colloquium: Sercan Yildiz, SAMSI

October 2, 2017 @ 3:30 pm - 4:30 pm

Title: Polynomial Optimization with Sums-of-Squares Interpolants Abstract: Sums-of-squares certificates define a hierarchy of relaxations for polynomial optimization problems which are parameterized with the degree of the polynomials in the sums-of-squares representation. Each level of the hierarchy generates a lower bound on the true optimal value, which can be computed in polynomial time via semidefinite programming, and these lower bounds converge to the true optimal value under mild assumptions. However, solving the semidefinite programs that arise from sums-of-squares relaxations poses practical…

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STOR Colloquium: Patrick Wolfe, Purdue University

October 9, 2017 @ 3:30 pm - 4:30 pm

Title: Nonparametric network comparison   Understanding how two networks differ, or quantifying the degree to which a single network departs from a given model, is a challenging question in modern mathematical statistics. Here we show how subgraph densities, which for large graphs play a role analogous to moments in the context of random variables, enable a natural means of nonparametric network comparison. Coupled with a partial order derived from a notion of subgraph scale, we then show how this leads…

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STOR Colloquium: Srinagesh Gavirneni, Cornell

October 16, 2017 @ 3:30 pm - 4:30 pm

Title: Co-opetition in Service Clusters with Waiting-Area Entertainment  Link to paper:  Co-opetition-Service-Clusters Abstract: Unoccupied waiting feels longer than it actually is. Service providers operationalize this psychological principle by offering entertainment options in waiting areas. In a service cluster with a shared waiting space, firms  have an opportunity to cooperate in the investment for providing entertainment options while competing on other service dimensions. In this paper, we develop a parsimonious model of co-opetition in a service cluster with shared entertainment options for waiting customers…

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STOR Colloquium: Jonathan Taylor, Stanford

October 23, 2017 @ 3:30 pm - 4:30 pm

Selective sampling after solving a convex problem   Recent work in the conditional approach to selective inference requires describing potentially complex conditional distributions. In this work, we describe a model-agnostic simplification to such conditional distributions when the selection stage can be expressed as a sequence of (randomized) convex programs with convex loss and structure inducing constraints or penalties. Our main result is a change of measure formula that expresses the selective likelihood in terms of an integral over variables appearing…

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STOR Colloquium: Holger Rootzén, Chalmers University of Technology

October 25, 2017 @ 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…

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November 2017

STOR Colloquium; Xiao-Li Meng, Harvard

November 6, 2017 @ 2:30 pm - 3:30 pm

Dissecting Multiple Imputation from a Multi-phase Inference Perspective: What Happens When God's, Imputer's and Analyst's Models Are Uncongenial?   Xiao-Li Meng Department of Statistics, Harvard University   This talk is based on a discussion paper (Xia and Meng, Statistica Sinica, 2017, pp1485-1594) with the same title and the following abstract:   “Real-life data are almost never really real. By the time the data arrive at an investigator's desk or disk, the raw data, however defined, have most likely gone through…

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