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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:20180311T070000
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DTSTART:20181104T060000
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180110T143000
DTEND;TZID=America/New_York:20180110T153000
DTSTAMP:20210511T194347
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:20210511T194347
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:20210511T194347
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:20210511T194347
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:20210511T194347
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:20210511T194347
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:20210511T194347
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:20210511T194347
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:20210511T194347
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:20210511T194347
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:20210511T194348
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:20210511T194348
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:20210511T194348
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:20210511T194348
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:20210511T194348
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:20210511T194348
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
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181008T153000
DTEND;TZID=America/New_York:20181008T163000
DTSTAMP:20210511T194348
CREATED:20180827T143857Z
LAST-MODIFIED:20180912T181939Z
UID:3823-1539012600-1539016200@stat-or.unc.edu
SUMMARY:STOR Colloquium: Rong Ge\, Duke University
DESCRIPTION:Rong Ge\nDuke University \n \nOptimization Landscape for Matrix Completion \nMatrix completion is a popular approach for recommendation systems. In theory\, it can be solved using complicated convex relaxations\, while in practice even simple algorithms such as stochastic gradient descent can always converge to the optimal solution. In this talk we will see some new results on the optimization landscape for the natural non-convex objective of matrix completion. In particular\, we will show that although the natural objective is non-convex and has many saddle points\, all of its local minima are equivalent to the global optimal solution. We will also discuss why such properties allow simple algorithms such as stochastic gradient descent to converge efficiently from an arbitrary initial point. \n \n \n \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-rong-ge-duke/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181015T153000
DTEND;TZID=America/New_York:20181015T163000
DTSTAMP:20210511T194348
CREATED:20180827T143945Z
LAST-MODIFIED:20181010T134944Z
UID:3825-1539617400-1539621000@stat-or.unc.edu
SUMMARY:STOR Colloquium: Cynthia Rudin\, Duke
DESCRIPTION:Cynthia Rudin \nDuke University \n \nNew Algorithms for Interpretable Machine Learning in High Stakes Decisions \n \nWith widespread use of machine learning\, there have been serious societal consequences from using black box models for high-stakes decisions\, including flawed models for medical imaging\, and poor bail and parole decisions in criminal justice. Explanations for black box models are not reliable\, and can be misleading. If we use interpretable models\, they come with their own explanations\, which are faithful to what the model actually computes. I will present work on (i) optimal decision lists\, (ii) interpretable neural networks for computer vision\, and (iii) optimal scoring systems (sparse linear models with integer coefficients). In our applications\, we have always been able to achieve interpretable models with the same accuracy as black box models. \n \nbio: Cynthia Rudin is an associate professor of computer science\, electrical and computer engineering\, and statistics at Duke University\, and directs the Prediction Analysis Lab. Her interests are in machine learning\, data mining\, applied statistics\, and knowledge discovery (Big Data)\, particularly interpretable machine learning. Her application areas are in energy grid reliability\, healthcare\, and computational criminology. Previously\, Prof. Rudin held positions at MIT\, Columbia\, and NYU. She holds an undergraduate degree from the University at Buffalo\, and a PhD in applied and computational mathematics from Princeton University. She is the recipient of the 2013 and 2016 INFORMS Innovative Applications in Analytics Awards\, an NSF CAREER award\, was named as one of the “Top 40 Under 40” by Poets and Quants in 2015\, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. Work from her lab has won 10 best paper awards in the last 5 years. She is past chair of the INFORMS Data Mining Section\, and is currently chair of the Statistical Learning and Data Science section of the American Statistical Association. She also serves on (or has served on) committees for DARPA\, the National Institute of Justice\, the National Academy of Sciences (for both statistics and criminology/law)\, and AAAI. \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-cynthia-rudin-duke/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181022T153000
DTEND;TZID=America/New_York:20181022T163000
DTSTAMP:20210511T194348
CREATED:20180906T141601Z
LAST-MODIFIED:20180906T141601Z
UID:3846-1540222200-1540225800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Robert Lund\, Clemson University
DESCRIPTION:STOR Colloquium \n\nRobert Lund\nDepartment of Mathematical Sciences \nClemson University \n\nMultiple Breakpoint Detection: Mixing Documented and Undocumented Changepoints \n|This talk presents methods to estimate the number of changepoint time(s) and their locations in time-ordered data sequences when prior information is known about some of the changepoint times. A Bayesian version of a penalized likelihood objective function is developed from minimum description length (MDL) information theory principles. Optimizing the objective function yields estimates of the changepoint number(s) and location time(s). Our MDL penalty depends on where the changepoint(s) lie\, but not solely on the total number of changepoints (such as classical AIC and BIC penalties). Specifically\, configurations with changepoints that occur relatively closely to one and other are penalized more heavily than sparsely arranged changepoints. The techniques allow for autocorrelation in the observations and mean shifts at each changepoint time. This scenario arises in climate time series where a “metadata” record exists documenting some\, but not necessarily all\, of station move times and instrumentation changes. Applications to climate time series are presented throughout. \n \n Refreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall \n
URL:https://stat-or.unc.edu/event/stor-colloquium-robert-lund-clemson-university/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181105T143000
DTEND;TZID=America/New_York:20181105T153000
DTSTAMP:20210511T194348
CREATED:20181025T122345Z
LAST-MODIFIED:20181025T122345Z
UID:3894-1541428200-1541431800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Sven Leyffer\, Argonne National Laboratory
DESCRIPTION:Mixed-Integer PDE-Constrained Optimization \n \nMany complex applications can be formulated as optimization problems constrained by partial differential equations (PDEs) with integer decision variables. This new class of problems\, called mixed-integer PDE-constrained optimization (MIPDECO)\, must overcome the combinatorial challenge of integer decision variables combined with the numerical and computational complexity of PDE-constrained optimization. Examples of MIPDECOs include the remediation of contaminated sites and the maximization of oil recovery; the design of next-generation solar cells; the layout design of wind-farms; the design and control of gas networks; disaster recovery; and topology optimization. \n \nWe will present some emerging applications of mixed-integer PDE-constrained optimization\, review existing approaches to solve these problems\, and \nhighlight their computational and mathematical challenges. We show how existing methods for solving mixed-integer optimization problems can be adapted to solve this new class of problems.
URL:https://stat-or.unc.edu/event/stor-colloquium-sven-leyffer-argonne-national-laboratory/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181107T143000
DTEND;TZID=America/New_York:20181107T153000
DTSTAMP:20210511T194348
CREATED:20181010T134459Z
LAST-MODIFIED:20181010T134459Z
UID:3876-1541601000-1541604600@stat-or.unc.edu
SUMMARY:STOR Colloquium: Quefeng Li\, UNC-Chapel Hill
DESCRIPTION:Quefeng Li \nDepartment of Biostatistics\nUNC-Chapel Hill \n \n \nIntegrative linear discriminant analysis with guaranteed error rate improvement \n \nNumerous empirical studies have found that integrative analysis of multimodal data can result in better statistical performance. However\, little theory is known on when and why including more variables in a statistical model can improve the prediction. In the context of two-class classification\, we provide a theoretical guarantee that running an integrative linear discriminant analysis on multimodal data achieves smaller misclassification error than running linear discriminant analysis on each individual data type. We explicitly characterize the trade-off between the extra information brought by multimodal data and the extra estimation error they bring. We also demonstrate that such a guarantee also applies to some other classifiers. \n \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-quefeng-li-unc-chapel-hill/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181112T143000
DTEND;TZID=America/New_York:20181112T153000
DTSTAMP:20210511T194348
CREATED:20180827T144038Z
LAST-MODIFIED:20181010T134552Z
UID:3827-1542033000-1542036600@stat-or.unc.edu
SUMMARY:STOR Colloquium: Xi Chen\, NYU
DESCRIPTION:Xi Chen\nNew York University \n \nStatistical Inference for Model Parameters with Stochastic Gradient Descent \n \nIn this talk\, we investigate the problem of statistical inference of the true model parameters based on stochastic gradient descent (SGD) with Ruppert-Polyak averaging. To this end\, we propose a consistent estimator of the asymptotic covariance of the average iterate from SGD — batch-means estimator\, which only uses the iterates from SGD. As the SGD process forms a time-inhomogeneous Markov chain\, our batch-means estimator with carefully chosen increasing batch sizes generalizes the classical batch-means estimator designed for time-homogenous Markov chains. The proposed batch-means estimator allows us to construct asymptotically exact confidence intervals and hypothesis tests. We further discuss an extension to conducting inference based on SGD for high-dimensional linear regression. \n \nBio: Xi Chen is an assistant professor at Stern School of Business at New York University. Before that\, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley. He obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University. He studies high-dimensional statistics\, multi-armed bandits\, and stochastic optimization. He received Simons-Berkeley Research Fellowship\, Google Faculty Award\, Adobe Data Science Award\, Bloomberg research award\, and was featured in 2017 Forbes list of “30 Under30 in Science”. \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-xi-chen-nyu/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181119T143000
DTEND;TZID=America/New_York:20181119T153000
DTSTAMP:20210511T194348
CREATED:20180827T144239Z
LAST-MODIFIED:20181116T201525Z
UID:3829-1542637800-1542641400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Tailen Hsing\, UMich
DESCRIPTION:Tailen Hsing\nDepartment of Statistics \nUniversity of Michigan \n \nModeling and inference of local stationarity \n \nStationarity is a common assumption in spatial statistics. The justification is often that stationarity is a reasonable approximation to the true state of dependence if we focus on spatial data locally. In this talk\, we first review various known approaches for modeling nonstationary spatial data. We then examine the notion of local stationarity in more detail. To illustrate\, we focus on the multi-fractional Brownian motion\, for which a thorough analysis could be conducted assuming data are observed on a regular grid. A theoretical lower bound for the minimax risk of this inference problem is established for a wide class of smooth Hurst functions. We also propose a new nonparametric estimator and show that it is rate optimal. Implementation issues of the estimator including how to overcome the presence of a nuisance parameter and choose the tuning parameter from data will be considered. Finally\, extensions to more general settings that relate to Matheron’s intrinsic random functions will be briefly discussed. \n \n \n \n \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-tailen-hsing-umich/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181128T143000
DTEND;TZID=America/New_York:20181128T153000
DTSTAMP:20210511T194348
CREATED:20180827T144351Z
LAST-MODIFIED:20181119T170729Z
UID:3831-1543415400-1543419000@stat-or.unc.edu
SUMMARY:STOR Colloquium: Weijie Su\, UPenn
DESCRIPTION:STOR Colloquium \nWednesday\, November 28th\, 2018 \n120 Hanes Hall \n3:30pm\n\nWeijie Su\nUniversity of Pennsylvania \nUncertainty Quantification for Stochastic Gradient Descent \n \nStochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However\, despite an ever-increasing volume of work on SGD\, much less is known about the statistical inferential properties of SGD-based predictions. Taking a fully inferential viewpoint\, this talk introduces a novel procedure termed HiGrad to conduct statistical inference for online learning\, without incurring additional computational cost compared with SGD. The HiGrad procedure begins by performing SGD updates for a while and then splits the single thread into several threads\, and this procedure hierarchically operates in this fashion along each thread. With predictions provided by multiple threads in place\, a t-based confidence interval is constructed by decorrelating predictions using covariance structures given by a Donsker-style extension of the Ruppert–Polyak averaging scheme\, which is a technical contribution of independent interest. Under certain regularity conditions\, the HiGrad confidence interval is shown to attain asymptotically exact coverage probability. The performance of HiGrad is evaluated through extensive simulation studies and a real data example. We conclude the talk with an application of HiGrad to deep neural networks. \nThis is based on joint work with Yuancheng Zhu. \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-weijie-su-upenn/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20181203T143000
DTEND;TZID=America/New_York:20181203T153000
DTSTAMP:20210511T194348
CREATED:20181119T171709Z
LAST-MODIFIED:20181119T171709Z
UID:3916-1543847400-1543851000@stat-or.unc.edu
SUMMARY:STOR Colloquium: Ana-Maria Staicu\, NC State
DESCRIPTION:STOR Colloquium \nMonday\, December 3rd\, 2018 \n120 Hanes Hall \n3:30pm\n\nAna-Maria Staicu\nNorth Carolina State University \nLongitudinal Dynamic Functional Regression \n \nIn this talk we discuss regression models to study the association between scalar outcomes and functional predictors observed over time\, at many instances\, in longitudinal studies. We propose a parsimonious modeling framework to study time-varying regression that leads to superior prediction properties and allows to reconstruct full trajectories of the response. The idea is to model the time-varying functional predictors using orthogonal basis functions and expand the time-varying regression coefficient using the same basis. Numerical investigation through simulation studies and data analysis show excellent performance in terms of accurate prediction and efficient computations\, when compared with existing alternatives. The methods are inspired and applied to an animal science application\, where of interest is to study the association between the feed intake of lactating sows and the minute-by-minute temperature throughout the 21st days of their lactation period. \n \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-ana-maria-staicu-nc-state/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190111T143000
DTEND;TZID=America/New_York:20190111T153000
DTSTAMP:20210511T194348
CREATED:20190102T184849Z
LAST-MODIFIED:20190102T220845Z
UID:3951-1547217000-1547220600@stat-or.unc.edu
SUMMARY:STOR Colloquium: Bikram Karmakar\, University of Pennsylvania
DESCRIPTION:Bikram Karmakar \nStatistics Department\, The Wharton School\,\nUniversity of Pennsylvania \n \nEvidence factors for observational studies: methodology\, computation and applications. \n \nObservational studies aim to elucidate cause-and-effect relationships from situations in which treatment is not randomly assigned. A sensitivity analysis for an observational study assesses how much bias\, due to non-random assignment of treatment\, would be necessary to change the conclusions of an analysis that assumes treatment assignment was effectively random. Causal conclusions gain strength from a demonstration that they are insensitive to small or moderate violations of non-random treatment assignment\, especially if that happens in each of several statistically independent analyses that depend upon very different assumptions. In particular\, causal conclusions gain strength when evidence factors concur and are insensitive to bias\, where a study is said to contain two or more evidence factors if it provides two or more tests of the null hypothesis of no treatment effect that would be (essentially) independent were there no effect. Previous work with evidence factors has not addressed the problem that they involve multiple testing and how to control the type-I error to obtain valid inference. We develop a powerful method for controlling the familywise error rate for sensitivity analyses with evidence factors. We show that the Bahadur efficiency of sensitivity analysis for the combined evidence is greater than for any one evidence factor alone\, so that even though using two or more evidence factors requires multiple testing\, a study is better off asymptotically using two or more evidence factors than just one factor. \n \nWe also develop methods to widen the applicability of evidence factors to various designs for causal assessment\, including designs with instrumental variables\, and case-control studies. Computationally however it is often very hard to build these designs optimally. Even the simplest addition to a one treatment-control comparison – a second comparison – creates design problems without polynomial-time solutions. We develop an “approximation algorithm” that provides a solution in polynomial time that is probably not much worse than the unattainable optimal solution. \n \nWe illustrate our methodological and computational developments for evidence factors in two observational studies: (i) the effect of exposure to radiation on solid cancers and (ii) the effect of having a side airbag on the chance of dying in a car crash. \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-bikram-karmakar/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190114T143000
DTEND;TZID=America/New_York:20190114T153000
DTSTAMP:20210511T194348
CREATED:20190102T185216Z
LAST-MODIFIED:20190102T223233Z
UID:3959-1547476200-1547479800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Joshua Cape\, Johns Hopkins
DESCRIPTION:Joshua Cape\nJohns Hopkins University \n \nStatistical analysis and spectral methods for signal-plus-noise matrix models \n \nEstimating eigenvectors and principal subspaces is of fundamental importance for numerous problems in statistics\, data science\, and network analysis\, including covariance matrix estimation\, principal component analysis\, and community detection. For each of these problems\, we obtain foundational results that precisely quantify the local (e.g.\, entrywise) behavior of sample eigenvectors within the context of a unified signal-plus-noise matrix framework. Our methods and results collectively address eigenvector consistency and asymptotic normality\, decompositions of high-dimensional matrices\, Procrustes analysis\, deterministic perturbation bounds\, and real-data spectral clustering applications in connectomics. \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-joshua-cape/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190116T143000
DTEND;TZID=America/New_York:20190116T153000
DTSTAMP:20210511T194348
CREATED:20190102T185436Z
LAST-MODIFIED:20190102T223102Z
UID:3965-1547649000-1547652600@stat-or.unc.edu
SUMMARY:STOR Colloquium: Yanglei Song\, University of Illinios
DESCRIPTION:Yanglei Song\nUniversity of Illinois at Urbana-Champaign \n Asymptotically optimal multiple testing with streaming data \n \nThe problem of testing multiple hypotheses with streaming (sequential) data arises in diverse applications such as multi-channel signal processing\, surveillance systems\, multi-endpoint clinical trials\, and online surveys. In this talk\, we investigate the problem under two generalized error metrics. Under the first one\, the probability of at least k mistakes\, of any kind\, is controlled. Under the second\, the probabilities of at least k1 false positives and at least k2 false negatives are simultaneously controlled. For each formulation\, we characterize the optimal expected sample size to a first-order asymptotic approximation as the error probabilities vanish\, and propose a novel procedure that is asymptotically efficient under every signal configuration. These results are established when the data streams for the various hypotheses are independent and each local log-likelihood ratio statistic satisfies a certain law of large numbers. Further\, in the special case of iid observations\, we quantify the asymptotic gains of sequential sampling over fixed-sample size schemes. \n \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-yanglei-song/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190118T143000
DTEND;TZID=America/New_York:20190118T153000
DTSTAMP:20210511T194348
CREATED:20190102T185356Z
LAST-MODIFIED:20190108T194040Z
UID:3963-1547821800-1547825400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Vince Lyzinski\, Univ. of Massachusetts Amherst
DESCRIPTION:Vince Lyzinski\nThe University of Massachusetts Amherst \n \nGraph matching in edge-independent networks \n \nThe graph matching problem seeks to find an alignment between the vertex sets of two graphs that best preserves common structure across graphs. Here\, we consider the closely related problem of graph matchability: Given a latent alignment between the vertex sets of two graphs\, under what conditions will the solution to the graph matching optimization problem recover this alignment in the presence of shuffled vertex labels? We consider the problem of graph matchability in non-identically distributed networks\, and working in a general class of edge-independent network models\, we demonstrate that graph matchability is almost surely lost when matching the networks directly\, and is almost perfectly recovered when first centering the networks using Universal Singular Value Thresholding before matching. While there are currently no efficient algorithms for solving the graph matching problem in general\, these results nonetheless provide practical algorithmic guidance for approximately matching networks in both real and synthetic data applications. \nRefreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall
URL:https://stat-or.unc.edu/event/stor-colloquium-vince-lyzinski/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190123T143000
DTEND;TZID=America/New_York:20190123T153000
DTSTAMP:20210511T194348
CREATED:20190102T185137Z
LAST-MODIFIED:20190116T144101Z
UID:3957-1548253800-1548257400@stat-or.unc.edu
SUMMARY:STOR Colloquium: Jingshen Wang\, University of Michigan
DESCRIPTION:Jingshen Wang\n University of Michigan \n \nInference on Treatment Effects after Model Selection \n \nInferring cause-effect relationships between variables is of primary importance in many sciences. In this talk\, I will discuss two approaches for making valid inference on treatment effects when a large number of covariates are present. The first approach is to perform model selection and then to deliver inference based on the selected model. If the inference is made ignoring the randomness of the model selection process\, then there could be severe biases in estimating the parameters of interest. While the estimation bias in an under-fitted model is well understood\, I will address a lesser known bias that arises from an over-fitted model. The over-fitting bias can be eliminated through data splitting at the cost of statistical efficiency\, and I will propose a repeated data splitting approach to mitigate the efficiency loss. The second approach concerns the existing methods for debiased inference. I will show that the debiasing approach is an extension of OLS to high dimensions\, and that a careful bias analysis leads to an improvement to further control the bias. The comparison between these two approaches provides insights into their intrinsic bias-variance trade-off\, and I will show that the debiasing approach may lose efficiency in observational studies. \n \nThis is joint work with Xuming He and Gongjun Xu. \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-jingshen-wang/
LOCATION:Hanes 120
CATEGORIES:STOR Colloquium
END:VEVENT
END:VCALENDAR