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March 2019

STOR Colloquium: Tucker McElroy, US Census Bureau

March 1, 2019 @ 2:30 pm - 3:30 pm

Tucker McElroy U.S. Census Bureau Casting Vector Time Series:  Forecasting, Imputation, and Signal Extraction In the Context of Big Data   Recursive algorithms, based upon the nested structure of Toeplitz covariance matrices arising from stationary processes, are presented for the efficient computation of multi-step ahead forecast error covariances for nonstationary vector time series. Further, we discuss time reversal to forecast the past, and algorithms for imputation of missing values. These quantities are required to quantify multi-step ahead forecast error and…

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STOR Colloquium: Jonathan M. Lees, UNC-Chapel Hill

March 4, 2019 @ 2:30 pm - 3:30 pm

Jonathan M. Lees University of North Carolina at Chapel Hill   Geophysical Time Series Analysis on Volcanoes: Can we quantify non-linearity? Most geophysical processes are aperiodic noisy, intermittent and transient.  This requires specialized methods for time series analysis, that seek patterns in time series that vary in space and time.  I present here examples from research on exploding volcanoes that exhibit tremor that appears to be resonant but likely results from nonlinear feedback systems.  The physical models for these observations…

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STOR Colloquium: Yuan Liao, Rutgers University

March 20, 2019 @ 3:30 pm - 4:30 pm

Yuan Liao Rutgers University     Factor-Driven Two-Regime Regression using Mixed Integer Programming   We propose a two-regime regression model where the switching between the regimes is driven by a vector of possibly unobservable factors. When the factors are latent, we estimate them by the principal component analysis of a much larger data set. We show that the optimization problem can be reformulated as mixed integer optimization and present two alternative computational algorithms: (1) MI quadratic programming and (2) MI…

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April 2019

STOR Colloquium: Serhan Ziya, UNC-Chapel Hill

April 1, 2019 @ 3:30 pm - 4:30 pm

Serhan Ziya University of North Carolina at Chapel Hill   Service operations with a focus in healthcare: a partial and subjective overview of related research in STOR    This talk will provide an overview of some of the research projects in which the speaker is either currently an active participant or hopes to help initiate in the near future. The main goal is to create awareness and generate interest in the area of service operations particularly as it relates to…

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September 2019

Colloquium: Peter J. Mucha, UNC-Chapel Hill

September 23, 2019 @ 3:30 pm - 4:30 pm

Peter J. Mucha The University of North Carolina at Chapel Hill Department of Mathematics Communities in Multilayer Networks Community detection describes the organization of a network in terms of patterns of connection, identifying tightly connected structures known as communities. A wide variety of methods for community detection have been proposed, with a number of software packages available for performing community detection. In the past decade, there has been increased interest in multilayer networks, a general framework that can be used…

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October 2019

STOR Colloquium: Tong Wang, University of Iowa

October 4, 2019 @ 3:30 pm - 4:30 pm

Dr. Tong Wang Tippie College of Business University of Iowa   Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model   Interpretable machine learning has received increasing interest in recent years, especially in domains where humans are involved in the decision-making process. However, the possible loss of the task performance for gaining interpretability is often inevitable, especially for large datasets or complicated tasks. This performance downgrade puts practitioners in a dilemma of choosing between a top-performing black-box…

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STOR Colloquium: Heping Zhang, Yale

October 14, 2019 @ 3:30 pm - 4:30 pm

Back to the Basics: Residuals and Diagnostics for Generalized Linear Models

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STOR Colloquium: Shujie Ma, UC Riverside

October 21, 2019 @ 3:30 pm - 4:30 pm

Shujie Ma University of California, Riverside   How many communities are there in a network?   Advances in modern technology have facilitated the collection of network data which emerge in many fields including biology, bioinformatics, physics, economics, sociology and so forth. Network data often have natural communities which are groups of interacting objects (i.e., nodes); pairs of nodes in the same group tend to interact more than pairs belonging to different groups. Community detection then becomes a very important task,…

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November 2019

STOR Colloquium: Jianqing Fan, Princeton

November 6, 2019 @ 3:30 pm - 4:30 pm

Jianqing Fan Princeton University   Statistical Inference on Membership Profiles in Large Networks   Network data is prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. The nodes can be broadly defined such as individuals, economic entities, documents, or medical disorders in social, economic, text, or health networks.  Yet a simple question of how to precisely quantify the statistical uncertainty associated with the identification of latent…

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January 2020

STOR Colloquium: Anna Little, Michigan State University

January 8, 2020 @ 3:30 pm - 4:30 pm

Robust Statistical Procedures for Noisy, High-dimensional Data   This talk addresses two topics related to robust statistical procedures for analyzing noisy, high-dimensional data: (I) path-based spectral clustering and (II) robust multi-reference alignment. Both methods must overcome a large ambient dimension and lots of noise to extract the relevant low dimensional data structure in a computationally efficient way. In (I), the goal is to partition the data into meaningful groups, and this is achieved by a novel approach which combines a…

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