Unless otherwise noted, all talks are in 120 Hanes Hall, at 3:30 PM on Mondays. Prior to the talk, from 3:00-3:30 PM, the audience is invited for refreshments in the lounge on the 3rd floor of Hanes Hall. If you would like to suggest a speaker, or get on our mailing list, please send an email to Dr. Gabor Pataki or Dr. Kai Zhang.

In addition to weekly colloquia and seminars, Hotelling lectures are held to honor the memory of Professor Harold Hotelling, first chairman of the “Department of Mathematical Statistics.”

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Upcoming Events › STOR Colloquium

November 2018

STOR Colloquium: Quefeng Li, UNC-Chapel Hill

November 7 @ 3:30 pm - 4:30 pm

Quefeng Li Department of Biostatistics UNC-Chapel Hill     Integrative linear discriminant analysis with guaranteed error rate improvement   Numerous 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…

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STOR Colloquium: Xi Chen, NYU

November 12 @ 3:30 pm - 4:30 pm

Xi Chen New York University   Statistical Inference for Model Parameters with Stochastic Gradient Descent   In 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…

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STOR Colloquium: Tailen Hsing, UMich

November 19 @ 4:30 pm

Tailen Hsing Department of Statistics University of Michigan   Modeling and inference of local stationarity   Stationarity 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…

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STOR Colloquium: Weijie Su, UPenn

November 28 @ 3:30 pm - 4:30 pm
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