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PRODID:-//Department of Statistics and Operations Research - ECPv4.4.1.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Statistics and Operations Research
X-ORIGINAL-URL:http://stat-or.unc.edu
X-WR-CALDESC:Events for Department of Statistics and Operations Research
BEGIN:VEVENT
DTSTART;TZID=UTC-4:20170301T153000
DTEND;TZID=UTC-4:20170301T163000
DTSTAMP:20170221T094926
CREATED:20170216T130137Z
LAST-MODIFIED:20170216T130137Z
UID:2767-1488382200-1488385800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Mariana Olvera-Cravioto\, University of California-Berkeley
DESCRIPTION:Directed complex networks and ranking algorithms\n\nIn the first part of this talk I will discuss a family of inhomogeneous directed random graphs for modeling complex networks such as the web graph\, Twitter\, ResearchGate\, and other social networks. This class of graphs includes as a special case the classical Erdos-Renyi model\, and can be used to replicate almost any type of predetermined degree distributions\, in particular\, power-law degrees such as those observed in most real-world networks. I will mention during the talk the main properties of this family of random graphs and explain how its parameters can be used to represent important data attributes that influence the connectivity of nodes in the network.\n\nIn the second part of the talk I will explain how ranking algorithms such as Google’s PageRank can be used to identify highly influential nodes in a network\, and present some recent results describing the distribution of the ranks computed by such algorithms. This work extends prior work done for the directed configuration model to the new class of inhomogeneous directed random graphs mentioned above\, and provides a more natural way to model the relationship between highly ranked nodes and their attributes. If time allows\, I will mention some interesting stochastic simulation challenges related to this problem.\n\n \n\nRefreshments will be served in the lounge area of Hanes Hall
URL:http://stat-or.unc.edu/event/stor-colloquium-mariana-olvera-cravioto-university-of-california-berkeley
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC-4:20170306T153000
DTEND;TZID=UTC-4:20170306T163000
DTSTAMP:20170221T094926
CREATED:20170117T150343Z
LAST-MODIFIED:20170216T130223Z
UID:2582-1488814200-1488817800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Zhiyi Zhang\, UNC Charlotte
DESCRIPTION:Title: Statistical Implications of Turing’s Formula\n\n \n\nAbstract: This talk is organized into three parts.\n\n \n\n \tTuring’s formula is introduced. Given an iid sample from a countable alphabet under a probability distribution\, Turing’s formula (introduced by Good (1953)\, hence also known as the Good-Turing formula) is a mind-bending non-parametric estimator of total probability associated with letters of the alphabet that are NOT represented in the sample. Many of its statistical properties were not clearly known for a stretch of nearly sixty years until recently. Some of the newly established results\, including various asymptotic normal laws\, are described.\n\n \n\n \tTuring’s perspective is described. Turing’s formula brought about a new perspective (or a new characterization) of probability distributions on general countable alphabets. The new perspective in turn provides a new way to do statistics on alphabets\, where the usual statistical concepts associated with random variables (on the real line) no longer exist\, for example\, moments\, tails\, coefficients of correlation\, characteristic functions don’t exist on alphabets (a major challenge of modern data sciences). The new perspective\, in the form of entropic basis\, is introduced.\n\n \n\n \tSeveral applications are presented\, including estimation of information entropy and diversity indices.\n
URL:http://stat-or.unc.edu/event/stor-colloquium-zhiyi-zhang-unc-charlotte
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC-4:20170327T153000
DTEND;TZID=UTC-4:20170327T163000
DTSTAMP:20170221T094926
CREATED:20170126T140445Z
LAST-MODIFIED:20170126T141711Z
UID:2613-1490628600-1490632200@stat-or.unc.edu
SUMMARY:Hotelling Lectures: Aad van der Vaart\, Leiden University
DESCRIPTION:Nonparametric Bayesian methods: frequentist analysis\nAad van der Vaart\nLeiden University\n\nWe present an overview of Bayesian methods to estimate functions or high-dimensional\nparameter vectors\, and discuss the validity (or not) of these methods from a\nnon-Bayesian point of view. For instance\, we consider using a Gaussian process\nas a prior for a regression function or (after exponentiation and normalisation) for a\ndensity function. We characterise the rate at which the corresponding posterior distribution\ncan recover a true function as the noise level tends to zero or the number of observations tends to infinity\,\nand discuss how this rate can be improved by scaling the time axis\, showing that an appropriate random\nscaling leads to adaptive recovery over a scale of smoothness levels. Recovery means that the posterior\ndistribution concentrates most of its mass near the parameter that generates the data\, for most\nobservations. It refers mostly to the location of the posterior distribution. A second use of the\nposterior distribution is uncertainty quantification\, and refers to the spread of the posterior distribution. In fact\,\nit is at the core of the Bayesian method to use the full posterior distribution as an indication\nof remaining uncertainty. We discuss the general difficulties of uncertainty quantification in\nnonparametric statistics\, from which Bayesian methods of course also cannot escape. We argue that\nthese difficulties imply that the uncertainty quantification of adaptive Bayesian methods must\nbe misleading for certain true parameters\, and present concrete examples.\nWe next show that for so-called self-similar parameters the uncertainty quantification is valid.\n\nThe first talk is a general introduction to these aspects of nonparametric Bayesian\nstatistics\, focused mostly at curve estimation. In the second talk we also address\nsimilar issues in the Bayesian recovery of regression parameters in sparse high-dimensional models.\n\nA reception will follow at 4:30PM in the 3rd floor lounge of Hanes Hall.
URL:http://stat-or.unc.edu/event/hotelling-lectures-aad-van-der-vaart-leiden-university
CATEGORIES:Hotelling Lectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC-4:20170329T153000
DTEND;TZID=UTC-4:20170329T170000
DTSTAMP:20170221T094926
CREATED:20170126T140726Z
LAST-MODIFIED:20170210T212649Z
UID:2615-1490801400-1490806800@stat-or.unc.edu
SUMMARY:Hotelling Lectures: Aad van der Vaart\, Leiden University
DESCRIPTION:Nonparametric Bayesian methods: frequentist analysis\nAad van der Vaart\nLeiden University\n\nA more detailed view of Bayesian methods to estimate functions or high-dimensional\nparameter vectors\, and discuss the validity (or not) of these methods from a\nnon-Bayesian point of view. For instance\, we consider using a Gaussian process\nas a prior for a regression function or (after exponentiation and normalisation) for a\ndensity function. We characterise the rate at which the corresponding posterior distribution\ncan recover a true function as the noise level tends to zero or the number of observations tends to infinity\,\nand discuss how this rate can be improved by scaling the time axis\, showing that an appropriate random\nscaling leads to adaptive recovery over a scale of smoothness levels. Recovery means that the posterior\ndistribution concentrates most of its mass near the parameter that generates the data\, for most\nobservations. It refers mostly to the location of the posterior distribution. A second use of the\nposterior distribution is uncertainty quantification\, and refers to the spread of the posterior distribution. In fact\,\nit is at the core of the Bayesian method to use the full posterior distribution as an indication\nof remaining uncertainty. We discuss the general difficulties of uncertainty quantification in\nnonparametric statistics\, from which Bayesian methods of course also cannot escape. We argue that\nthese difficulties imply that the uncertainty quantification of adaptive Bayesian methods must\nbe misleading for certain true parameters\, and present concrete examples.\nWe next show that for so-called self-similar parameters the uncertainty quantification is valid.\n\nThe first talk is a general introduction to these aspects of nonparametric Bayesian\nstatistics\, focused mostly at curve estimation. In the second talk we also address\nsimilar issues in the Bayesian recovery of regression parameters in sparse high-dimensional models.\n\n \n\nRefreshments will be served prior to the lecture at 3:00PM in the 3rd floor lounge of Hanes Hall.
URL:http://stat-or.unc.edu/event/hotelling-lectures-aad-van-der-vaart-leiden-university-2
CATEGORIES:Hotelling Lectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC-4:20170403T153000
DTEND;TZID=UTC-4:20170403T163000
DTSTAMP:20170221T094926
CREATED:20170117T150432Z
LAST-MODIFIED:20170215T210854Z
UID:2584-1491233400-1491237000@stat-or.unc.edu
SUMMARY:STOR Colloquium: Hongtu Zhu\, MD Anderson
DESCRIPTION:
URL:http://stat-or.unc.edu/event/stor-colloquium-hongtu-zhu-md-anderson
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC-4:20170410T153000
DTEND;TZID=UTC-4:20170410T163000
DTSTAMP:20170221T094926
CREATED:20170117T150518Z
LAST-MODIFIED:20170215T210854Z
UID:2586-1491838200-1491841800@stat-or.unc.edu
SUMMARY:STOR Colloquium: Philip Ernst\, Rice University
DESCRIPTION:
URL:http://stat-or.unc.edu/event/stor-colloquium-philip-ernst-rice-university
CATEGORIES:STOR Colloquium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC-4:20170417T153000
DTEND;TZID=UTC-4:20170417T163000
DTSTAMP:20170221T094926
CREATED:20170117T150611Z
LAST-MODIFIED:20170215T210854Z
UID:2588-1492443000-1492446600@stat-or.unc.edu
SUMMARY:STOR Colloquium: Ilse Ipsen\, North Carolina State University
DESCRIPTION:
URL:http://stat-or.unc.edu/event/stor-colloquium-ilse-ipsen-north-carolina-state-university
CATEGORIES:STOR Colloquium
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