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X-WR-CALNAME:Department of Statistics
X-ORIGINAL-URL:https://stat.wisc.edu
X-WR-CALDESC:Events for Department of Statistics
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TZID:America/Chicago
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TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20200308T080000
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DTSTART:20201101T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20200122T160000
DTEND;TZID=America/Chicago:20200122T160000
DTSTAMP:20200119T171626
CREATED:20200113T170915Z
LAST-MODIFIED:20200117T195907Z
UID:2722-1579708800-1579708800@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: Navigation and Evaluation in High-Dimensional Data \nPresenter: Kris Sankaran \nAbstract: In the modern data analysis paradigm\, fitting models is easy\, but knowing how to design or evaluate them is difficult. In this talk\, we will adapt insights from graphical statistics and goodness-of-fit testing to modern problems. We motivate and illustrate our methodology through real-world applications in microbiome genomics and climate systems science — the data we will encounter are rich in tree\, spatial\, and temporal structure. \nThis structure complicates the three practical tasks around which this talk revolves: data exploration\, model formulation\, and model evaluation. For the microbiome\, we show how linking complementary displays can make it easy to interactively query structure in raw data. Our implementation is available as an R package\, treelapse. We then describe connections between microbiome and text data\, and how those connections suggest novel modeling strategies\, visual summaries\, and diagnostics. The experiments leading to our recommendations can be reproduced at https://github.com/krisrs1128/microbiome_plvm. Finally\, we explain how artificial intelligence can be used to accelerate climate simulations\, and introduce techniques for characterizing goodness-of-fit of the resulting models\, inspired by Neyman’s smooth test. \nViewed broadly\, these projects provide opportunities for human interaction in the automated data processing regime\, facilitating (1) streamlined navigation of data and (2) critical evaluation of models. \n
URL:https://stat.wisc.edu/event/statistics-seminar-4/
LOCATION:133 SMI
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20200124T160000
DTEND;TZID=America/Chicago:20200124T170000
DTSTAMP:20200119T171626
CREATED:20200113T170915Z
LAST-MODIFIED:20200117T200009Z
UID:2724-1579881600-1579885200@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: Kriging: Beyond Matérn \nPresenter: Pulong Ma \nAbstract: Satellite instruments and computer models that simulate physical processes of interest often lead to massive amount of data with complicated structures. Statistical analysis of such data needs to deal with a wide range of challenging problems such as high-dimensionality and nonstationarity. To understand and predict real-world processes\, kriging\, originated in geostatistics in the 1960s\, has been widely used for prediction in spatial statistics and uncertainty quantification (UQ). In the first part of my talk\, I shall give a brief overview of my research related to kriging or Gaussian process regression to tackle these challenging issues in various real-world applications. In the second part of my talk\, I shall introduce a new family of covariance functions to perform kriging. Over the past several decades\, the Matérn covariance function has been a popular choice to model dependence structures. A key benefit of the Matérn class is that it is possible to get precise control over the degree of differentiability of the process realizations. However\, the Matérn class possesses exponentially decaying tails\, and thus may not be suitable for modeling long range dependence. This problem can be remedied using polynomial covariances; however\, one loses control over the degree of differentiability of the process realizations\, in that the realizations using polynomial covariances are either infinitely differentiable or not differentiable at all. To overcome this dilemma\, a new family of covariance functions is constructed using a scale mixture representation of the Matérn class where one obtains the benefits of both Matérn and polynomial covariances. The resultant covariance contains two parameters: one controls the degree of differentiability near the origin and the other controls the tail heaviness\, independently of each other. This new covariance function also enjoys nice theoretical properties under infill asymptotics including equivalence measures\, asymptotic behavior of the maximum likelihood estimators\, and asymptotically efficient prediction under misspecified models. The improved theoretical properties in predictive performance of this new covariance class are verified via extensive simulations. Application using NASA’s Orbiting Carbon Observatory-2 satellite data confirms the advantage of this new covariance class over the Matérn class\, especially in extrapolative settings. This talk concludes with discussions on extrapolation in UQ studies. \n
URL:https://stat.wisc.edu/event/statistics-seminar-4-2020-01-24/
LOCATION:133 SMI
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20200127T160000
DTEND;TZID=America/Chicago:20200127T170000
DTSTAMP:20200119T171626
CREATED:20200113T170915Z
LAST-MODIFIED:20200117T193505Z
UID:2725-1580140800-1580144400@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: \nPresenter: \nAbstract: \n
URL:https://stat.wisc.edu/event/statistics-seminar-4-2020-01-27/2020-01-27/
LOCATION:133 SMI
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20200129T160000
DTEND;TZID=America/Chicago:20200129T170000
DTSTAMP:20200119T171626
CREATED:20200113T170915Z
LAST-MODIFIED:20200117T193505Z
UID:2726-1580313600-1580317200@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: \nPresenter: \nAbstract: \n
URL:https://stat.wisc.edu/event/statistics-seminar-4-2020-01-27/2020-01-29/
LOCATION:133 SMI
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20200131T160000
DTEND;TZID=America/Chicago:20200131T170000
DTSTAMP:20200119T171626
CREATED:20200113T170915Z
LAST-MODIFIED:20200117T193505Z
UID:2727-1580486400-1580490000@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: \nPresenter: \nAbstract: \n
URL:https://stat.wisc.edu/event/statistics-seminar-4-2020-01-27/2020-01-31/
LOCATION:133 SMI
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