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X-WR-CALNAME:Department of Statistics and Operations Research
X-ORIGINAL-URL:https://stat-or.unc.edu
X-WR-CALDESC:Events for Department of Statistics and Operations Research
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DTSTART:20170312T070000
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170828T153000
DTEND;TZID=America/New_York:20170828T163000
DTSTAMP:20210515T193725
CREATED:20170811T203540Z
LAST-MODIFIED:20170822T182947Z
UID:3086-1503934200-1503937800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Ion Necoara\, Polytechnic University of Bucharest
DESCRIPTION:Ion Necoara \nPolytechnic University of Bucharest \n \nConditions for linear convergence of (stochastic) first order methods \n \nFor 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 still has a special structure. In this talk we replace the smoothness/strong convexity conditions with several other conditions\, that are less conservative\, for which we are able to prove that several (stochastic) first order methods are converging linearly. We also provide necessary conditions for linear convergence of (stochastic) gradient method. Finally\, we discuss several applications of these results (Lasso problem\, linear systems\, linear programming\, convex feasibility\, etc). \n \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall \n
URL:https://stat-or.unc.edu/event/stor-colloquium-ion-necoara/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170906T153000
DTEND;TZID=America/New_York:20170906T163000
DTSTAMP:20210515T193725
CREATED:20170829T135641Z
LAST-MODIFIED:20170831T193636Z
UID:3122-1504711800-1504715400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Hao Chen\, UC Davis
DESCRIPTION:Change-point detection for locally dependent data \n \nLocal 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 graph-based scan statistic for locally dependent data. We derive accurate analytic approximations to the significance of graph-based scan statistics under the circular block permutation framework\, facilitating its application to locally dependent multivariate or object data sequences. \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-hao-chen-uc-davis/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170911T153000
DTEND;TZID=America/New_York:20170911T163000
DTSTAMP:20210515T193725
CREATED:20170829T135845Z
LAST-MODIFIED:20170911T191934Z
UID:3124-1505143800-1505147400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Iain Carmichael\, UNC-Chapel Hill
DESCRIPTION:Title: Data science and the undergraduate curriculum \n \nLast 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 number of ways including: more code than math\, focus on real data/questions\, a final project\, and open source course material. We present the overall goals of the class\, the teaching methods\, design choices and our own takeaways from the class. Drawing on our own experiences and a survey of the literature on advancing the undergraduate statistics curriculum we discuss future directions for both this course and the rest of the curriculum. \nLink to presentation slides: \nhttps://docs.google.com/presentation/d/1XUaNIybiPD6OpTs-ou5baSUQYiUOJuafXQsvEwrChjc/edit?usp=sharing \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-iain-davis-unc-chapel-hill/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170918T153000
DTEND;TZID=America/New_York:20170918T163000
DTSTAMP:20210515T193725
CREATED:20170829T165631Z
LAST-MODIFIED:20170908T200142Z
UID:3126-1505748600-1505752200@stat-or.unc.edu
SUMMARY:STOR Colloquium: Philip Ernst\, Rice
DESCRIPTION:Yule’s “Nonsense Correlation” Solved! \n \nIn 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 value for the standard deviation of the empirical correlation of nearly .5. The “nonsense” correlation\, which we call “volatile” correlation\, is volatile in the sense that its distribution is heavily dispersed and is frequently large in absolute value. It is induced because each Wiener process is “self-correlated” in time. This is because a Wiener process is an integral of pure noise and thus its values at different time points are correlated. In addition to providing an explicit formula for the second moment of the empirical correlation\, we offer implicit formulas for higher moments of the empirical correlation. The full paper is currently in press at The Annals of Statistics and can be found at https://projecteuclid.org/euclid.aos/1498636874. \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-philip-ernst-rice/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171002T153000
DTEND;TZID=America/New_York:20171002T163000
DTSTAMP:20210515T193725
CREATED:20170811T203647Z
LAST-MODIFIED:20170905T165354Z
UID:3088-1506958200-1506961800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Sercan Yildiz\, SAMSI
DESCRIPTION:Title: Polynomial Optimization with Sums-of-Squares Interpolants\nAbstract: 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 challenges at higher levels of the hierarchy. First\, the sizes of these semidefinite programs depend quadratically on the number of monomials in the sums-of-squares representations. Second\, numerical problems are often encountered. In this talk\, we show that non-symmetric conic programming and polynomial interpolation techniques can be used to optimize efficiently over the sums-of-squares cone. Preliminary computational results indicate that our method compares favorably against standard approaches. The talk is based on joint work with David Papp.
URL:https://stat-or.unc.edu/event/stor-colloquium-sercan-yildiz/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171009T153000
DTEND;TZID=America/New_York:20171009T163000
DTSTAMP:20210515T193725
CREATED:20170811T203752Z
LAST-MODIFIED:20170926T171305Z
UID:3090-1507563000-1507566600@stat-or.unc.edu
SUMMARY:STOR Colloquium: Patrick Wolfe\, Purdue University
DESCRIPTION:Title: Nonparametric network comparison\n \nUnderstanding 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 to an automated\, computationally scalable comparison algorithm with provable properties. \nJoint work with P.-A. Maugis and S. C. Olhede; preprint at https://arxiv.org/abs/1705.05677. \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-patrick-wolfe-purdue/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171016T153000
DTEND;TZID=America/New_York:20171016T163000
DTSTAMP:20210515T193725
CREATED:20170811T204013Z
LAST-MODIFIED:20171009T183335Z
UID:3093-1508167800-1508171400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Srinagesh Gavirneni\, Cornell
DESCRIPTION:Title: Co-opetition in Service Clusters with Waiting-Area Entertainment \n Link to paper: Co-opetition-Service-Clusters\n \nAbstract: 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 (e.g.\, a boardwalk). By comparing the case of co-opetition with two benchmarks (monopoly\, and duopoly competition)\, we demonstrate that a service provider\, which would otherwise be a local monopolist\, can achieve a higher pro fit by joining a service cluster and engaging in co-opetition: we numerically show that the average firm profit under co-opetition is 7.65% higher than under monopoly. Achieving such benefits\, however\, requires a cost-allocation scheme properly addressing a fairness-efficiency tradeoff. A pursuit of fairness may backfire and lead to even lower profits than under pure competition. We show that as much as co-opetition facilitates resource sharing in a service cluster\, it also heightens price competition. Furthermore\, as the intensity of price competition increases\, surprisingly\, service providers may opt to charge higher service fees\, albeit while providing a higher entertainment level. \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall \nNagesh Gavirneni is a professor of operations management in the Samuel Curtis Johnson Graduate School of Management at Cornell University. His research interests are in the areas of supply chain management\, inventory control\, production scheduling\, simulation and optimization. He is now using these models and methodologies to solve problems in healthcare\, agriculture and humanitarian logistics in developing countries. Previously\, he was an assistant professor in the Kelley School of Business at Indiana University\, the chief algorithm design engineer of SmartOps\, a Software Architect at Maxager Technology\, Inc. and a research scientist with Schlumberger. He has an undergraduate degree in Mechanical Engineering from IIT-Madras\, a Master’s degree from Iowa State University\, and a Ph.D. from Carnegie Mellon University. \n
URL:https://stat-or.unc.edu/event/stor-colloquium-srinagesh-gavirneni-cornell/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171023T153000
DTEND;TZID=America/New_York:20171023T163000
DTSTAMP:20210515T193725
CREATED:20170811T204130Z
LAST-MODIFIED:20171004T173406Z
UID:3095-1508772600-1508776200@stat-or.unc.edu
SUMMARY:STOR Colloquium: Jonathan Taylor\, Stanford
DESCRIPTION:Selective sampling after solving a convex problem \n \nRecent 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 in the optimization problem. The region of integration can often be interpreted geometrically in terms of the normal cycle of the balls in the corresponding penalty. Using this change of measure\, we give a brief description of “inferactive data analysis”\, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis.\nThis is joint work with Xiaoying Tian\, Jelena Markovic\, Snigdha Panigrahi and Nan Bi. \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-jonathan-taylor-stanford/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171025T153000
DTEND;TZID=America/New_York:20171025T163000
DTSTAMP:20210515T193725
CREATED:20171012T192535Z
LAST-MODIFIED:20171012T192919Z
UID:3300-1508945400-1508949000@stat-or.unc.edu
SUMMARY:STOR Colloquium: Holger Rootzén\, Chalmers University of Technology
DESCRIPTION:Human life is unlimited — but short \n \nDoes 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 even as indication of a decreasing limit\, but also as evidence that a limit does not exist. This talk uses EVS\, extreme value statistics\, to study what data says about human mortality after age 110. We show that in North America\, Western Europe\, and Japan the yearly probability of dying after age 110 is constant and about 53% per year. Hence there is no finite limit to the human lifespan. Still\, given the present stage of biotechnology\, it is unlikely that during the next 25 years anyone will live longer than 128 years in these countries. Data\, remarkably\, show little difference in mortality after age 110 between men and women\, between earlier and later periods\, between ages\, or between persons with different lifestyles or genetic backgrounds. These results can help testing biological theories of aging and aid early confirmation of success of efforts to find a cure for aging. \n \nThis is joint work with Dmitrii Zholud. \n \n \n \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-holger-rootzen-chalmers-university-of-technology/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171106T143000
DTEND;TZID=America/New_York:20171106T153000
DTSTAMP:20210515T193725
CREATED:20170829T165906Z
LAST-MODIFIED:20171030T195736Z
UID:3128-1509978600-1509982200@stat-or.unc.edu
SUMMARY:STOR Colloquium; Xiao-Li Meng\, Harvard
DESCRIPTION:Dissecting Multiple Imputation from a Multi-phase Inference Perspective: \nWhat Happens When God’s\, Imputer’s and Analyst’s Models Are Uncongenial? \n \nXiao-Li Meng \nDepartment of Statistics\, Harvard University \n \nThis talk is based on a discussion paper (Xia and Meng\, Statistica Sinica\, 2017\, pp1485-1594) with the same title and the following abstract: \n \n“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 at least one “cleaning” process\, such as standardization\, re-calibration\, imputation\, or de-sensitization. Dealing with such a reality scientifically requires a more holistic multi-phase perspective than is permitted by the usual framework of “God’s model versus my model.” This article provides an in-depth look\, from this broader perspective\, into multiple-imputation (MI) inference (Rubin (1987)) under uncongeniality (Meng (1994)). We present a general estimating-equation decomposition theorem\, resulting in an analytic (asymptotic) description of MI inference as an integration of the knowledge of the imputer and the analyst\, and establish a characterization of self-efficiency (Meng (1994)) for regulating estimation procedures. These results help to reveal how the quality of and relationship between the imputer’s model and analyst’s procedure affect MI inference\, including how a seemingly perfect procedure under the “God-versus-me” paradigm is actually inadmissible when God’s\, imputer’s\, and analyst’s models are uncongenial to each other. Our theoretical investigation also leads to useful procedures that are as trivially implementable as Rubin’s combining rules\, yet with confidence coverage guaranteed to be minimally the nominal level\, under any degree of uncongeniality. We reveal that the relationship is very complex between the validity of approaches taken for individual phases and the validity of the final multi-phase inference\, and indeed that it is a nontrivial matter to quantify or even qualify the meaning of validity itself in such settings. These results and many open problems are presented to raise the general awareness that the multi-phase inference paradigm is an uncongenial forest populated by thorns\, as well as some fruits\, many of which are still low-hanging.”
URL:https://stat-or.unc.edu/event/stor-colloquium-xiao-li-meng-harvard/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171113T143000
DTEND;TZID=America/New_York:20171113T153000
DTSTAMP:20210515T193725
CREATED:20171031T154103Z
LAST-MODIFIED:20171031T203308Z
UID:3319-1510583400-1510587000@stat-or.unc.edu
SUMMARY:STOR Colloquium: Danica Ommen\, Iowa State University
DESCRIPTION:Title: Different Paradigms of Interpretation for Forensic Value of Evidence Quantification \nAbstract: \nCurrently\, one of the major problems in the forensic science community is the confusion between different statistical paradigms. A quantification of the value of evidence is interpreted differently under each paradigm\, and may even be the answer to different questions. It is our opinion that these issues need to be addressed before quantitative forensic analyses are considered a reliable science in the justice system. A related issue is the blending of paradigms that so often occurs during the statistical analysis for forensic evidence. Many statisticians will use techniques that are a combination of methods from different paradigms. When this occurs\, there is often an issue of interpreting and successfully applying a result that is somewhere on the spectrum between paradigms. This presentation will focus on three different paradigms of evidence interpretation\, the Bayesian\, the Frequentist\, and the Likelihoodist. The appropriate methods for quantifying the value of evidence under each paradigm and the corresponding interpretation of the result will be discussed. The presentation will conclude with some guidelines of how to “safely” mix the paradigms.
URL:https://stat-or.unc.edu/event/stor-colloquium-danica-ommen-iowa-state-university/
LOCATION:Hanes Hall
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171115T143000
DTEND;TZID=America/New_York:20171115T153000
DTSTAMP:20210515T193725
CREATED:20170822T182806Z
LAST-MODIFIED:20170831T192456Z
UID:3104-1510756200-1510759800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Todd Kuffner\, Washington University in St. Louis
DESCRIPTION:Title: Philosophy of Science\, Principled Statistical Inference\, and Data Science \n \nAbstract: 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. \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-todd-kuffner-washington-university-in-st-louis/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171120T143000
DTEND;TZID=America/New_York:20171120T153000
DTSTAMP:20210515T193725
CREATED:20170829T170239Z
LAST-MODIFIED:20171010T202203Z
UID:3132-1511188200-1511191800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Alex Belloni\, Duke
DESCRIPTION:Title: Inference with High-Dimensional Controls and Parameters of Interest \nbased on joint work with Victor Chernozhukov\, Denis Chetverikov\, and Ying Wei \nAbstract: 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 a function. The procedure is based on the construction of generalized score functions which are new moment functions with an additional orthogonality condition. The proposed uniform confidence bands for all parameters relies on uniform central limit theorems for high-dimensional vectors (and not on Donsker arguments as we allow for p > n). The construction of the bands are based on a multiplier bootstrap procedure which is computationally efficient as it only involves resampling the estimated score functions (and does not require resolving the high-dimensional optimization problems). We formally apply the general theory to inference on regression coefficient process in the distribution regression model with a logistic link\, where two implementations are analyzed in detail. Simulations and an application to real data are provided to help illustrate the applicability of the results.
URL:https://stat-or.unc.edu/event/stor-colloquium-alex-belloni-duke/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20171204T143000
DTEND;TZID=America/New_York:20171204T153000
DTSTAMP:20210515T193725
CREATED:20170829T170407Z
LAST-MODIFIED:20171128T200640Z
UID:3134-1512397800-1512401400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Anru Zhang\, University of Wisconsin-Madison
DESCRIPTION:Singular Value Decomposition for High-dimensional High-order Data \n \nHigh-dimensional high-order data arise in many modern scientific applications including genomics\, brain imaging\, and social science. In this talk\, we consider the methods\, theories and computations for tensor singular value decomposition (tensor SVD)\, which aims to extract the hidden low-rank structure from high-dimensional high-order data. First\, comprehensive results are developed on both the statistical and computational limits for tensor SVD under the general scenario. This problem exhibits three different phases according to signal-noise-ratio (SNR)\, and the minimax-optimal statistical and/or computational results are developed in each of the regimes. In addition\, we further consider the sparse tensor singular value decomposition which allows more robust estimation under sparsity structural assumptions. A novel sparse tensor alternating thresholding algorithm is proposed. Both the optimal theoretical results and numerical analyses are provided to guarantee the performance of the proposed procedure. \n \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-anru-zhang-university-of-wisconsin-madison/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180110T143000
DTEND;TZID=America/New_York:20180110T153000
DTSTAMP:20210515T193725
CREATED:20180117T165104Z
LAST-MODIFIED:20180117T165104Z
UID:3349-1515594600-1515598200@stat-or.unc.edu
SUMMARY:Mariana Olvera-Cravioto\, University of California\, Berkeley
DESCRIPTION:Efficient simulation for branching recursions \n \nA variety of problems in science and engineering\, ranging from population and statistical physics models to the study of queueing systems\, computer networks and the internet\, lead to the analysis of branching distributional equations. The solutions to these equations are not in general analytically tractable\, and hence need to be computed numerically. This talk discusses a simulation algorithm known as “Population Dynamics”\, which is designed to produce a pool of identically distributed observations having approximately the same law as the attracting endogenous solution in a wide class of branching distributional equations. \n \nThe Population Dynamics algorithm repeatedly uses bootstrap to move from one iteration of the branching distributional equation to the next\, which dramatically reduces the exponential complexity of the naïve Monte Carlo approach. We present new results guaranteeing the convergence of the Wasserstein distance between the distribution of the pool generated by the algorithm and that of the true attracting endogenous solution. \n \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/mariana-olvera-cravioto-university-of-california-berkeley/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180117T143000
DTEND;TZID=America/New_York:20180117T153000
DTSTAMP:20210515T193725
CREATED:20180117T165511Z
LAST-MODIFIED:20180117T171355Z
UID:3353-1516199400-1516203000@stat-or.unc.edu
SUMMARY:CANCELLED: Jie Ding\, Harvard University
DESCRIPTION:Some New Foundational Principles and Fast Algorithms \nin Data Analytics \n \nRapid developments in communications\, networking\, AI robots\, 3D printing\, genomics\, blockchain\, novel materials\, and powerful computation platforms are rapidly bringing data-generating people\, processes and devices together. The interactions between data analytics in multiple regimes (sparse\, panel\, big data\, etc.) and other fields are exciting because the tools that are being invented now may enable new\, faster and semi-automated methods of scientific discovery. These\, in turn\, might further amplify the pace of progress and significantly impact various areas of health\, science and technology.\nMotivated by the above\, my research has been focused on the modeling\, representation\, detection and prediction from data. In this seminar\, I will introduce some of my past research under the framework of a universal data analytic engine “TimeHunter” that I have been developing. In particular\, I will introduce some new foundational principles and efficient algorithms in the areas of model selection\, nonlinear time series\, change detection\, and multi-regime analysis. I will focus on the key idea of each contribution\, and demonstrate their performance with both synthetic and real data. \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/jie-ding-harvard-university/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180119T143000
DTEND;TZID=America/New_York:20180119T153000
DTSTAMP:20210515T193725
CREATED:20180117T165323Z
LAST-MODIFIED:20180119T153507Z
UID:3351-1516372200-1516375800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Mikael Kuusela\, SAMSI and UNC-Chapel Hill
DESCRIPTION:Locally stationary spatio-temporal interpolation of Argo profiling float data \n \nArgo floats measure sea water temperature and salinity in the upper 2\,000 m of the global ocean. The statistical analysis of the resulting spatio-temporal data set is challenging due to its nonstationary structure and large size. I propose mapping these data using locally stationary Gaussian process regression where covariance parameter estimation and spatio-temporal prediction are carried out in a moving-window fashion. This yields computationally tractable nonstationary anomaly fields without the need to explicitly model the nonstationary covariance structure. I also investigate Student-t distributed microscale variation as a means to account for non-Gaussian heavy tails in Argo data. Cross-validation studies comparing the proposed approach with the existing state-of-the-art demonstrate clear improvements in point predictions and show that accounting for the nonstationarity and non-Gaussianity is crucial for obtaining well-calibrated uncertainties. The approach also provides data-driven local estimates of the spatial and temporal dependence scales for the global ocean which are of scientific interest in their own right. \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/mikael-kuusela-samsi-and-unc-chapel-hill/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180122T143000
DTEND;TZID=America/New_York:20180122T153000
DTSTAMP:20210515T193725
CREATED:20180117T164908Z
LAST-MODIFIED:20180122T154830Z
UID:3347-1516631400-1516635000@stat-or.unc.edu
SUMMARY:STOR Colloquium: Jason Xu\, UCLA
DESCRIPTION:Enabling likelihood-based inference for complex and dependent data \n \nThe likelihood function is central to many statistical procedures\, but poses challenges in classical and modern data settings. Motivated by emergent cell lineage tracking experiments to study blood cell production\, we present recent methodology enabling likelihood-based inference for partially observed data arising from continuous-time stochastic processes with countable state space. These computational advances allow principled procedures such as maximum likelihood estimation\, posterior inference\, and expectation-maximization (EM) algorithms in previously intractable data settings. We then discuss limitations and alternatives when data are very large or generated from a hidden process\, and address some of the remaining challenges using optimization. We highlight majorization-minimization (MM) algorithms\, a generalization of EM\, showcasing their merits and breadth on related problems including likelihood-based approaches for sparse and low-rank estimation \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall \n
URL:https://stat-or.unc.edu/event/jason-xu-ucla/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180124T143000
DTEND;TZID=America/New_York:20180124T153000
DTSTAMP:20210515T193725
CREATED:20180117T165726Z
LAST-MODIFIED:20180123T213104Z
UID:3355-1516804200-1516807800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Shizhe Chen\, Columbia University
DESCRIPTION:Learning the Connectivity of Large Sets of Neurons \n \nNew techniques in neuroscience have opened the door to rich new data sets of neural activities. These data sets shed light on the computational foundation of the brain\, i.e.\, neurons and synapses. However\, these data also present unprecedented challenges: novel statistical theory and methods are required to model neural activities\, and well-designed experiments are needed to collect informative data. In this talk\, we take on the task of learning connectivity among large sets of neurons. In particular\, we discuss i) how to learn functional connectivity from spike train data using the Hawkes process\, and ii) how to optimally design experiments to collect data that allow us\, for the first time\, to learn physiological connectivity in vivo. \n \n \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/shizhe-chen/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180126T143000
DTEND;TZID=America/New_York:20180126T153000
DTSTAMP:20210515T193725
CREATED:20180117T165829Z
LAST-MODIFIED:20180123T213209Z
UID:3357-1516977000-1516980600@stat-or.unc.edu
SUMMARY:STOR Colloquium: Robin Gong\, Harvard University
DESCRIPTION:Bayes is sensitive. Is imprecise probability more sensible? \n \nBayes is prized as principled and coherent\, but its quality of inference is sensitive to prior and model misspecifications. Imprecise probability (IP) allows for the flexible expression of partially deficient probabilistic information. In our quest for minimal-assumption inference\, is IP a more promising alternative to Bayes? \n \nIn this talk\, I showcase the power of IP with an application of the Dempster-Shafer theory of belief functions to the prior-free estimation of infection time in acute HIV-1 patients. I discuss the non-trivial choice of IP updating rules (generalized Bayes\, Dempster’s and Geometric) in relation to prominent behavioral features that do not occur in precise model updating\, including dilation\, contraction and sure loss. The results reinforce a “no free lunch” principle. IP abandons the prior at the expense of abandoning the Bayes rule\, introducing a new kind of sensitivity that manifests the trade-off between inference risk and payoff. \n \nNo prior knowledge of IP is required of the audience\, but a willingness to contribute opinions on whether it is more sensible\, in the name of conducting scientifically defensible inference\, to trade a choice of priors for a choice of rules in a series of hypothetical and real examples. \n \nBio: Ruobin (Robin) Gong is a PhD student in statistics at Harvard University\, advised by Xiao-Li Meng and Arthur Dempster. Her research focuses on the theoretical\, methodological and computational aspects of statistical modeling with imprecise probabilities\, random sets\, and the Dempster-Shafer theory of belief functions. Robin also has a broad interest in scientific applications in bioinformatics\, astrostatistics\, neuroscience\, and education. She holds a B.Sc. in cognitive psychology from the University of Toronto. Webpage: https://scholar.harvard.edu/rgong\nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/robin-gong/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180129T143000
DTEND;TZID=America/New_York:20180129T153000
DTSTAMP:20210515T193725
CREATED:20180117T165937Z
LAST-MODIFIED:20180123T213434Z
UID:3359-1517236200-1517239800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Sara Algeri\, Imperial College London
DESCRIPTION:Testing One Hypothesis Multiple Times \n \nThe identification of new rare signals in data\, the detection of a sudden change in a trend\, and the selection of competing models\, are some among the most challenging problems in statistical practice. In this talk I discuss how these challenges can be tackled using a test of hypothesis where a nuisance parameter is present only under the alternative\, and how a computationally efficient solution can be obtained by Testing One Hypothesis Multiple times (TOHM). Specifically\, a fine discretization of the space of the non-identifiable parameter is specified\, and evidence in support of the null or alternative hypothesis is obtained by approximating the distribution of the supremum of the resulting stochastic process (or random field). The methodology proposed is highly generalizable and combines elements of extreme value theory\, graph theory and simulations methods to achieve ease of implementation and computational efficiency. Applications are discussed in the context of bump-hunting\, break-point regression and non-nested models comparisons. \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/sara-algeri/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180202T143000
DTEND;TZID=America/New_York:20180202T153000
DTSTAMP:20210515T193725
CREATED:20180123T215429Z
LAST-MODIFIED:20180123T215429Z
UID:3378-1517581800-1517585400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Jie Ding\, Harvard University
DESCRIPTION:Some New Foundational Principles and Fast Algorithms in Data Analytics \n \nRapid developments in communications\, networking\, AI robots\, 3D printing\, genomics\, blockchain\, novel materials\, and powerful computation platforms are rapidly bringing data-generating people\, processes and devices together. The interactions between data analytics in multiple regimes (sparse\, panel\, big data\, etc.) and other fields are exciting because the tools that are being invented now may enable new\, faster and semi-automated methods of scientific discovery. These\, in turn\, might further amplify the pace of progress and significantly impact various areas of health\, science and technology.\nMotivated by the above\, my research has been focused on the modeling\, representation\, detection and prediction from data. In this seminar\, I will introduce some of my past research under the framework of a universal data analytic engine “TimeHunter” that I have been developing. In particular\, I will introduce some new foundational principles and efficient algorithms in the areas of model selection\, nonlinear time series\, change detection\, and multi-regime analysis. I will focus on the key idea of each contribution\, and demonstrate their performance with both synthetic and real data. \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-jie-ding-harvard-university/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180205T143000
DTEND;TZID=America/New_York:20180205T153000
DTSTAMP:20210515T193725
CREATED:20180123T215632Z
LAST-MODIFIED:20180123T215632Z
UID:3380-1517841000-1517844600@stat-or.unc.edu
SUMMARY:STOR Colloquium: Jason Klusowski\, Yale University
DESCRIPTION:Counting connected components and motifs of large graphs via graph sampling \n \nLearning properties of large graphs from samples has been an important problem in statistical network analysis since the early work of Goodman (1949) and Frank (1978). We revisit a problem formulated by Frank of estimating the number of connected components in a large graph based on the subgraph sampling model\, in which we randomly sample a subset of the vertices and observe the induced subgraph. The key question is whether accurate estimation is achievable in the sublinear regime where only a vanishing fraction of the vertices are sampled. We show that it is impossible if the parent graph is allowed to contain high-degree vertices or long induced cycles. For the class of chordal graphs\, where induced cycles of length four or above are forbidden\, we characterize the optimal sample complexity within constant factors and construct linear-time estimators that provably achieve these bounds. This significantly expands the scope of previous results which have focused on unbiased estimators and special classes of graphs such as forests or cliques. \n \nBoth the construction and the analysis of the proposed methodology rely on combinatorial properties of chordal graphs and identities of induced subgraph counts. They\, in turn\, also play a key role in proving minimax lower bounds based on construction of random instances of graphs with matching structures of small subgraphs. \n \nWe will also discuss results for the neighborhood sampling model\, where we additionally observe the edges between the sampled vertices and their neighbors. In this setting\, we will show how to construct optimal estimators of motif counts that are adaptive to certain unknown graph parameters. \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-jason-klusowski-yale-university/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180219T143000
DTEND;TZID=America/New_York:20180219T153000
DTSTAMP:20210515T193725
CREATED:20180215T155532Z
LAST-MODIFIED:20180215T155532Z
UID:3411-1519050600-1519054200@stat-or.unc.edu
SUMMARY:STOR Colloquium: Kai Zhang\, UNC-Chapel Hill
DESCRIPTION:Kai Zhang\nUniversity of North Carolina\, Chapel Hill\n \nBET on Independence \n \nWe study the problem of nonparametric dependence detection. Many existing methods suffer severe power loss due to non-uniform consistency\, which we illustrate with a paradox. To avoid such power loss\, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat) and binary expansion testing (BET)\, which examine dependence through a novel binary expansion filtration approximation of the copula. Through a Hadamard transform\, we find that the cross interactions of binary variables in the filtration are complete sufficient statistics for dependence. These interactions are also uncorrelated under the null. By utilizing these interactions\, the BET avoids the problem of non-uniform consistency and improves upon a wide class of commonly used methods (a) by achieving the minimax rate in sample size requirement for reliable power and (b) by providing clear interpretations of global relationships upon rejection of independence. The binary expansion approach also connects the test statistics with the current computing system to facilitate efficient bitwise implementation. We illustrate the BET with a study of the distribution of stars in the night sky and with an exploratory data analysis of the TCGA breast cancer data. \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall \n
URL:https://stat-or.unc.edu/event/stor-colloquium-kai-zhang-unc-chapel-hill/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180228T143000
DTEND;TZID=America/New_York:20180228T153000
DTSTAMP:20210515T193725
CREATED:20180223T173753Z
LAST-MODIFIED:20180223T173753Z
UID:3433-1519828200-1519831800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Rick Kelly\, Marsh & McLennan Agency\, LLC
DESCRIPTION:STOR Colloquium \nWednesday\, February 28th\, 2018 \n120 Hanes Hall \n3:30pm\n\nRick Kelly\nFSA Division Manager\, \nMarsh & McLennan Agency\, LLC\n\n\nSo you think you want to be an actuary?\nThe actuarial profession is often promoted as a great career\, but what does an actuarial analyst actually do? Led by the actuarial leadership of a national consulting firm\, this course will introduce students to the healthcare industry and provide hands-on experience with key actuarial and analytical concepts that apply across the actuarial field. Using real world situations\, the course will focus on how mathematics and the principles of risk management are used to help insurance companies and employers make better decisions regarding employee benefit insurance products and programs. An emphasis on group project work will enable students to develop and present recommendations\, better preparing them for their initial position after graduation. \n \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-rick-kelly-marsh-mclennan-agency-llc/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180319T153000
DTEND;TZID=America/New_York:20180319T163000
DTSTAMP:20210515T193725
CREATED:20180305T201712Z
LAST-MODIFIED:20180305T201712Z
UID:3464-1521473400-1521477000@stat-or.unc.edu
SUMMARY:STOR Colloquium: Terry Soo\, University of Kansas
DESCRIPTION:The Department of \nStatistics and Operations Research \nThe University of North Carolina at Chapel Hill \n \n \nSTOR Colloquium \nMonday\, March 19th\, 2018 \n120 Hanes Hall \n3:30pm\n\nTerry Soo\nUniversity of Kansas \n \nISOMORPHISMS IN PROBABILITY AND ERGODIC THEORY \n \nTwo measure-preserving systems are isomorphic if there exists a measure-preserving bijection between them that respects the dynamics of the systems. Kolmogorov (1958) showed that Shannon entropy is an isomorphism invariant for independent and identically distributed systems\, and Ornstein (1970) showed it is in fact a complete invariant. A simpler proof of Ornstein’s result for i.i.d. systems was given by Keane and Smorodinsky (1979). \n \nAs part of a general theory for the isomorphism problem for actions of an amenable group\, Ornstein and Weiss (1987) proved that two Poisson point processes are isomorphic. I will discuss ongoing work with Amanda Wilkens\, where we give an elementary proof of the result of Ornstein and Weiss. I will also discuss other probabilistic variants of Ornstein theory. \n \n \n \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-terry-soo-university-of-kansas/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180430T153000
DTEND;TZID=America/New_York:20180430T163000
DTSTAMP:20210515T193725
CREATED:20180425T194222Z
LAST-MODIFIED:20180425T194222Z
UID:3604-1525102200-1525105800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Louigi Addario-Berry\, McGill University
DESCRIPTION:Louigi Addario-Berry\nMcGill University \n \nAssumptionless bounds for Galton-Watson trees and \nrandom combinatorial trees. \n \nLet T be any Galton-Watson tree. Write vol(T) for the volume of T (the number of nodes)\, ht(T) for the height of T (the greatest distance of any node from the root) and wid(T) for the width of T (the greatest number of nodes at any level). We study the relation between vol(T)\, ht(T) and wid(T). \n \nIn the case when the offspring distribution p = (p_i\, i \geq 0) has mean one and finite variance\, both ht(T) and wid(T) are typically of order vol(T)^{1/2}\, and have sub-Gaussian upper tails on this scale (A-B\, Devroye and Janson\, 2013). Heuristically\, as the tail of the offspring distribution becomes heavier\, the tree T becomes “shorter and bushier”. I will describe a collection of work which can be viewed as justifying this heuristic in various ways In particular\, I will explain how classical bounds on the Lévy’s concentration function for random walks may be used to show that the random variable ht(T)/wid(T) always has sub-exponential tails. I will also describe a more combinatorial approach to coupling random trees with different degree sequences which allows the heights of randomly sampled vertices to be compared. \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall \n
URL:https://stat-or.unc.edu/event/stor-colloquium-louigi-addario-berry-mcgill-university/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180827T153000
DTEND;TZID=America/New_York:20180827T163000
DTSTAMP:20210515T193725
CREATED:20180827T134759Z
LAST-MODIFIED:20180827T134759Z
UID:3811-1535383800-1535387400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Richard Smith\, UNC-CH
DESCRIPTION:The Department of \nStatistics and Operations Research \nThe University of North Carolina at Chapel Hill \n \n \nSTOR Colloquium \nMonday\, August 27th\, 2018 \n120 Hanes Hall \n3:30pm\n\nRichard Smith\nUniversity of North Carolina-Chapel Hill\n\nThe First Anniversary of Hurricane Harvey \n \nThis weekend marks exactly one year since Hurricane Harvey devastated much of the Caribbean and then dumped record levels of rainfall on the city of Houston and its environs. This event also stimulated much scientific work focused on two major issues of a statistical nature\, (a) trying to assess just how extreme the Harvey rainfalls were\, and (b) assessing to what extent the extremeness of Harvey could be attributed to anthropogenic climate change\, and the related question of how common such events are likely to be in the future. In joint work with Ken Kunkel of the North Carolina Institute of Climate Studies\, I have fitted extreme value models to extreme precipitation events in the southeast US and have linked these both to increasing temperatures in the Gulf of Mexico and to global levels of carbon dioxide\, both of which have anthropogenic origins. To complement these applied topics\, I will also review more theoretical developments stimulated by similar problems. These are (i) extreme value theory for spatial processes\, (b) the statistical theory surrounding the detection and attribution of anthropogenic signals in both observed and model-generated climate data. \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall \n \n
URL:https://stat-or.unc.edu/event/stor-colloquium-richard-smith-unc-ch/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180926T153000
DTEND;TZID=America/New_York:20180926T163000
DTSTAMP:20210515T193725
CREATED:20180827T143715Z
LAST-MODIFIED:20180912T180326Z
UID:3819-1537975800-1537979400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Tingting Zhang\, University of Virginia
DESCRIPTION:Tingting Zhang\nUniversity of Virginia \n\nA Bayesian Stochastic-Blockmodel-based Approach for Mapping Epileptic Brain Networks \n \nThe human brain is a dynamic system consisting of many consistently interacting regions. The brain regions and the influences exerted by each region over another\, called directional connectivity\, form a directional network. We study normal and abnormal directional brain networks of epileptic patients using their intracranial EEG (iEEG) data\, which are multivariate time series recordings of many small brain regions. We propose a high-dimensional state-space multivariate autoregression model (SSMAR) for iEEG data. To characterize brain networks with a commonly reported cluster structure\, we use a stochastic-block-model-motivated prior for possible network patterns in the SSMAR. We develop a Bayesian framework to estimate the proposed high-dimensional model\, examine the probabilities of nonzero directional connectivity among every pair of regions\, identify clusters of densely-connected brain regions\, and map epileptic patients’ brain networks in different seizure stages. We show through both simulation and real data analysis that the new method outperforms existing network methods by being flexible to characterize various high-dimensional network patterns and robust to violation of model assumptions\, low iEEG sampling frequency\, and data noise. Applying the developed SSMAR and Bayesian approach to an epileptic patient’s iEEG data\, we reveal the patient’s network changes at the seizure onset and the unique connectivity of the seizure onset zone (SOZ)\, where seizures start and spread to other normal regions. Using this network result\, our method has a potential to assist clinicians to localize the SOZ\, a long standing research focus in epilepsy diagnosis and treatment. \n \n \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall \n
URL:https://stat-or.unc.edu/event/stor-colloquium-tingting-zhang-university-of-virginia/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181001T153000
DTEND;TZID=America/New_York:20181001T163000
DTSTAMP:20210515T193725
CREATED:20180827T143807Z
LAST-MODIFIED:20180918T172158Z
UID:3821-1538407800-1538411400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Vinayak Deshpande\, UNC Kenan Flagler
DESCRIPTION:Vinayak Deshpande \nKenan Flagler Business School\, \nUniversity of North Carolina at Chapel Hill \n \nData Driven Research: Understanding and Improving Airline Flight Schedules using BTS data \n \nThe last decade has seen an explosion of operational data that is now available to researchers. In this talk\, I will share my experience in conducting research with large datasets made publicly available by the Bureau of Transportation Statistics (BTS). These data sets include flight schedule data\, FAA operations and performance data\, DOT’s domestic airline fares consumer report\, and the T-100 domestic market data. These data sets provide granular information on airline operations in the United States. My prior research has used this data to model the intrinsic uncertainty in the travel time of any commercially scheduled domestic flight in the United States\, as well as built models of how this uncertainty propagates in airline networks. These data driven models can be used to understand airline flight schedules through a descriptive lens\, as well as in building prescriptive models for improving airline flight schedules. I will summarize my findings on airline flight schedules in this talk\, as well as discuss potential research opportunities that can use this data. \n Links to relevant papers: \nReliable Air Travel Infrastructure\n \nAirline Flight Delays \n \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-vinayak-deshpande-unc-kenan-flagler/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
END:VCALENDAR