The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.

Bottom-up estimation and top-down prediction in multi-level models: Solar Energy Prediction combining information from multiple sources

Accurately forecasting solar power using a statistical method from multiple sources is an important but challenging problem. Our goal is to combine two different physics model forecasting outputs with real measurements from an automated monitoring network so as to better predict solar power in a timely manner. To this end, we propose a bottom-up approach of analyzing large-scale multilevel models with great computational efficiency requiring minimum monitoring and intervention.

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Developing and Using Electronic Assessments to Inform Student Learning and Instruction in Introductory Statistics

As a part of a multi-year NSF funded project (DUE 12-45504), we developed electronic vocabulary, clicker and homework assessments at the topic level for a broad range of introductory statistics courses. In this talk, I will discuss the development and structure of these assessments, including the selection of topics, specification of learning outcomes and vocabulary words, and principles used in the development of the assessment questions. I will then describe some of the research on student learning from this work and its impact on instruction in our introductory statistics course.

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A Frequency Domain Empirical Likelihood Method for Irregularly Spaced Spatial Data

In this talk, we consider empirical likelihood methodology for irregularly spaced spatial data in the frequency domain. The main result of the paper shows that upto a suitable (and nonstandard) scaling, Wilk’s phenomenon holds for the logarithm of the empirical likelihood ratio in the sense that it is asymptotically distribution free and has a chi-squared limit. As a result, the proposed spatial FDEL method can be used to build nonparametric, asymptotically correct confidence regions and tests for a class spectral parameters that are defined through spectral estimating equations.

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Yao Xie

 Yao Xie joined Georgia Institute of Technology as an Assistant Professor in the H. Milton Stewart School of Industrial & Systems Engineering in 2013. Prior to that, she worked as a Research Scientist at Duke University in the Department of Electrical and Computer Engineering, after receiving her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University in 2011. She is interested in signal processing, sequential analysis (e.g.

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Anindya Roy

We present a reparameterization of vector autoregressive moving average (VARMA) models that allows estimation of parameters under the constraints of causality and invertibility. The parameter constraints associated with a causal invertible VARMA model are highly complex. An m-variate VARMA(p; q) process contains (p+q)m2 + m(m+1)/2 parameters, which must be constrained to a complicated subset of the Euclidean space in order to guarantee causality, invertibility. The main result of the paper is a bijection from the constrained set to the entire Euclidean space.

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Detecting Nonlinear Relationships via Slicing

I will discuss a few recent results from my group aiming to the detection of non-linear dependence and interactive effects of several random variables. These approaches were all developed by taking a Bayesian viewpoint on the inverse-slicing idea first proposed by Ker-Chau Li. We will also show how these methods are applied to bioinformatics problems such as gene-set enrichment analysis, transcriptional regulation analysis, etc.

http://en.wikipedia.org/wiki/Jun_Liu

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David Banks

The last decade has seen substantial progress in topic modeling, and considerable progress in the study of dynamic networks.  This research combines these threads, so that the network structure informs topic discovery and the identified topics predict network behavior.  The data consist of text and links from all U.S. political blogs curated by Technorati during the calendar year 2012.  A particular advantage of the model used in this research is that it naturally enforces cluster structure in the topics, through a block model for the bloggers.

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